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