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 "subspace robust wasserstein distances"):
  Abstract: Making sense of Wasserstein distances between discrete measures in high-dimensional settings remains a challenge. Recent work has advocated a two-step approach to improve robustness and facilitate the computation of optimal transport, using for instance projections on random real lines, or a preliminary quantization of the measures to reduce the size of their support. We propose in this work a "max-min" robust variant of the Wasserstein distance by considering the maximal possible distance that can be realized between two measures, assuming they can be projected orthogonally on a lower $k$-dimensional subspace. Alternatively, we show that the corresponding "min-max" OT problem has a tight convex relaxation which can be cast as that of finding an optimal transport plan with a low transportation cost, where the cost is alternatively defined as the sum of the $k$ largest eigenvalues of the second order moment matrix of the displacements (or matchings) corresponding to that plan (the usual OT definition only considers the trace of that matrix). We show that both quantities inherit several favorable properties from the OT geometry. We propose two algorithms to compute the latter formulation using entropic regularization, and illustrate the interest of this approach empirically.

Includes members:
  Member "sliced wasserstein autoencoder an embarrassingly simple generative model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning implicit generative models with theoretical guarantees")
  Member "generative modeling using the sliced wasserstein distance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning implicit generative models with theoretical guarantees")
  Member "approximating the quadratic transportation metric in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a direct tilde o 1 epsilon iteration parallel algorithm for optimal transport")
  Member "advances in pre training distributed word representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "sliced wasserstein kernels for probability distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning autoencoders with relational regularization")
  Member "learning probability measures with respect to optimal transport metrics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "model imitation for model based reinforcement learning")
  Member "explicit approximations of the gaussian kernel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalization guarantees for sparse kernel approximation with entropic optimal features")

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)