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 "deep scale spaces equivariance over scale"):
  Abstract: We introduce deep scale-spaces (DSS), a generalization of convolutional neural networks, exploiting the scale symmetry structure of conventional image recognition tasks. Put plainly, the class of an image is invariant to the scale at which it is viewed. We construct scale equivariant cross-correlations based on a principled extension of convolutions, grounded in the theory of scale-spaces and semigroups. As a very basic operation, these cross-correlations can be used in almost any modern deep learning architecture in a plug-and-play manner. We demonstrate our networks on the Patch Camelyon and Cityscapes datasets, to prove their utility and perform introspective studies to further understand their properties.

Includes members:
  Member "roto translation covariant convolutional networks for medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attentive group equivariant convolutional networks")
  Member "tensor field networks rotation and translation equivariant neural networks for 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on learning sets of symmetric elements")
  Member "a general theory of equivariant cnns on homogeneous spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on learning sets of symmetric elements")
  Member "scale equivariance in cnns with vector fields":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attentive group equivariant convolutional networks")
  Member "rotation equivariant cnns for digital pathology":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "learning steerable filters for rotation equivariant cnns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attentive group equivariant convolutional networks")
  Member "dilated residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "learning so 3 equivariant representations with spherical cnns":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a rotation invariant framework for deep point cloud analysis")

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)