human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "earth mover s distance yields positive definite kernels for certain ground distances"
  Abstract: Positive definite kernels are an important tool in machine learning that enable efficient solutions to otherwise difficult or intractable problems by implicitly linearizing the problem geometry. In this paper we develop a set-theoretic interpretation of the Earth Mover's Distance (EMD) that naturally yields metric and kernel forms of EMD as generalizations of elementary set operations. In particular, EMD is generalized to sets of unequal size. We also offer the first proof of positive definite kernels based directly on EMD, and provide propositions and conjectures concerning what properties are necessary and sufficient for EMD to be conditionally negative definite. In particular, we show that three distinct positive definite kernels -- intersection, minimum, and Jaccard index -- can be derived from EMD with various ground distances. In the process we show that the Jaccard index is simply the result of a positive definite preserving transformation that can be applied to any kernel. Finally, we evaluate the proposed kernels in various computer vision tasks.

Connected through hyperedge HE-1 (induced by source paper "sliced wasserstein kernels for probability distributions"):
  with members:
    - "on the definiteness of earth mover s distance and its relation to set intersection"
    - "the radon cumulative distribution transform and its application to image classification"
    - "the cumulative distribution transform and linear pattern classification"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "kernel methods on the riemannian manifold of symmetric positive definite matrices"
    - "geodesic exponential kernels when curvature and linearity conflict"

Question: Please tell me which class the center node 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)