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 "sliced wasserstein kernels for probability distributions"):
  Abstract: Optimal transport distances, otherwise known as Wasserstein distances, have recently drawn ample attention in computer vision and machine learning as a powerful discrepancy measure for probability distributions. The recent developments on alternative formulations of the optimal transport have allowed for faster solutions to the problem and has revamped its practical applications in machine learning. In this paper, we exploit the widely used kernel methods and provide a family of provably positive definite kernels based on the Sliced Wasserstein distance and demonstrate the benefits of these kernels in a variety of learning tasks. Our work provides a new perspective on the application of optimal transport flavored distances through kernel methods in machine learning tasks.

Includes members:
  Member "on the definiteness of earth mover s distance and its relation to set intersection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wasserstein weisfeiler lehman graph kernels")
  Member "the radon cumulative distribution transform and its application to image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "max sliced wasserstein distance and its use for gans")
  Member "the cumulative distribution transform and linear pattern classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sliced wasserstein distance for learning gaussian mixture models")
  Member "earth mover s distance yields positive definite kernels for certain ground distances":
    also connected through other hyperedges:
      (none)
  Member "kernel methods on riemannian manifolds with gaussian rbf kernels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "person re identification in aerial imagery")
  Member "kernel methods on the riemannian manifold of symmetric positive definite matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "identity preserving face recovery from stylized portraits")
  Member "geodesic exponential kernels when curvature and linearity conflict":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning")

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)