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The Hubble Space Telescope (HST) survey Measuring Young Stars in Space andTime (MYSST) entails some of the deepest photometric observations ofextragalactic star formation, capturing even the lowest mass stars of theactive star-forming complex N44 in the Large Magellanic Cloud. We employ thenew MYSST stellar catalog to identify and characterize the content of youngpre-main-sequence (PMS) stars across N44 and analyze the PMS clusteringstructure. To distinguish PMS stars from more evolved line of sightcontaminants, a non-trivial task due to several effects that alter photometry,we utilize a machine learning classification approach. This consists oftraining a support vector machine (SVM) and a random forest (RF) on a carefullyselected subset of the MYSST data and categorize all observed stars as PMS ornon-PMS. Combining SVM and RF predictions to retrieve the most robust set ofPMS sources, we find $\sim26,700$ candidates with a PMS probability above 95%across N44. Employing a clustering approach based on a nearest neighbor surfacedensity estimate, we identify 18 prominent PMS structures at $1$ $\sigma$significance above the mean density with sub-clusters persisting up to andbeyond $3$ $\sigma$ significance. The most active star-forming center, locatedat the western edge of N44's bubble, is a subcluster with an effective radiusof $\sim 5.6$ pc entailing more than 1,100 PMS candidates. Furthermore, weconfirm that almost all identified clusters coincide with known H II regionsand are close to or harbor massive young O stars or YSOs previously discoveredby MUSE and Spitzer observations.

Measuring Young Stars in Space and Time -- II. The Pre-Main-Sequence Stellar Content of N44