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 "gauge equivariant mesh cnns anisotropic convolutions on geometric graphs"):
  Abstract: A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs). Such GCNs utilize isotropic kernels and are therefore insensitive to the relative orientation of vertices and thus to the geometry of the mesh as a whole. We propose Gauge Equivariant Mesh CNNs which generalize GCNs to apply anisotropic gauge equivariant kernels. Since the resulting features carry orientation information, we introduce a geometric message passing scheme defined by parallel transporting features over mesh edges. Our experiments validate the significantly improved expressivity of the proposed model over conventional GCNs and other methods.

Includes members:
  Member "deep scale spaces equivariance over scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attentive group equivariant convolutional networks")
  Member "general e 2 equivariant steerable cnns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attentive group equivariant convolutional networks")
  Member "gauge equivariant convolutional networks and the icosahedral cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on learning sets of symmetric elements")
  Member "spherical u net on cortical surfaces methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images")
  Member "spiralnet a fast and highly efficient mesh convolution operator":
    also connected through other hyperedges:
      (none)
  Member "zernet convolutional neural networks on arbitrary surfaces via zernike local tangent space estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an efficient framework for visible infrared cross modality person re identification")
  Member "parallel transport convolution a new tool for convolutional neural networks on manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gauge equivariant convolutional networks and the icosahedral cnn")
  Member "roto translation covariant convolutional networks for medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attentive group equivariant convolutional networks")

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)