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Excess of gamma-rays with a spherical morphology around the Galactic center(GC) observed in the Fermi Large Area Telescope (LAT) data is one of the mostintriguing features in the gamma-ray sky. The spherical morphology and thespectral energy distribution with a peak around a few GeV are consistent withemission from annihilation of dark matter particles. Other possibleexplanations include a distribution of millisecond pulsars (MSPs). One of thecaveats of the MSP hypothesis is the relatively small number of associated MSPsnear the GC. In this paper we perform a multi-class classification of Fermi-LATsources using machine learning and determine the contribution from MSP-likesources among unassociated Fermi-LAT sources near the GC. We find that theunassociated MSP-like sources have a spectral energy distribution that iscomparable with the GC excess if the contribution from the Fermi bubbles istaken into account. The spatial morphology and the source count distributionare also consistent with expectations for a population of MSPs that can explainthe gamma-ray excess. Possible caveats of the contribution from theunassociated MSP-like sources include uncertainties in the diffuse emissionmodel that can affect the number and the properties of the point-like sourcesdetected near the GC and the contribution of the Fermi bubbles.

Resolving Galactic center GeV excess with MSP-like sources