human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "x ray sobolev variational auto encoders"):
  Abstract: The quality of the generative models (Generative adversarial networks, Variational Auto-Encoders, ...) #R##N#depends heavily on the choice of a good #R##N#probability distance. However some popular metrics #R##N#lack convenient properties such as (geodesic) convexity, fast evaluation and so on. #R##N#To address these shortcomings, we introduce a class of distances that have built-in convexity. #R##N#We investigate the relationship with some known paradigms (sliced distances, #R##N#reproducing kernel Hilbert spaces, energy distances).#R##N#The distances are shown to posses fast implementations and#R##N#are included  in an adapted Variational Auto-Encoder #R##N#termed X-ray Sobolev Variational Auto-Encoder (XS-VAE) which produces good quality results#R##N#on standard generative datasets.

Includes members:
  Member "sliced wasserstein generative models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fdftnet facing off fake images using fake detection fine tuning network")
  Member "max sliced wasserstein distance and its use for gans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image hashing by minimizing independent relaxed wasserstein distance")
  Member "generative modeling using the sliced wasserstein distance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning implicit generative models with theoretical guarantees")
  Member "sliced wasserstein distance for learning gaussian mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regularized wasserstein means for aligning distributional data")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)