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 "geodesic exponential kernels when curvature and linearity conflict"):
  Abstract: We consider kernel methods on general geodesic metric spaces and provide both negative and positive results. First we show that the common Gaussian kernel can only be generalized to a positive definite kernel on a geodesic metric space if the space is flat. As a result, for data on a Riemannian manifold, the geodesic Gaussian kernel is only positive definite if the Riemannian manifold is Euclidean. This implies that any attempt to design geodesic Gaussian kernels on curved Riemannian manifolds is futile. However, we show that for spaces with conditionally negative definite distances the geodesic Laplacian kernel can be generalized while retaining positive definiteness. This implies that geodesic Laplacian kernels can be generalized to some curved spaces, including spheres and hyperbolic spaces. Our theoretical results are verified empirically.

Includes members:
  Member "kernel methods on riemannian manifolds with gaussian rbf kernels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "identity preserving face recovery from stylized portraits")
  Member "optimizing over radial kernels on compact manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sketch specific data augmentation for freehand sketch recognition")
  Member "the varifold representation of nonoriented shapes for diffeomorphic registration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "white matter fiber analysis using kernel dictionary learning and sparsity priors")
  Member "shortest path distance in random k nearest neighbor graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "face feasible and actionable counterfactual explanations")

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)