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 "general e 2 equivariant steerable cnns"):
  Abstract: The big empirical success of group equivariant networks has led in recent years to the sprouting of a great variety of equivariant network architectures. A particular focus has thereby been on rotation and reflection equivariant CNNs for planar images. Here we give a general description of $E(2)$-equivariant convolutions in the framework of Steerable CNNs. The theory of Steerable CNNs thereby yields constraints on the convolution kernels which depend on group representations describing the transformation laws of feature spaces. We show that these constraints for arbitrary group representations can be reduced to constraints under irreducible representations. A general solution of the kernel space constraint is given for arbitrary representations of the Euclidean group $E(2)$ and its subgroups. We implement a wide range of previously proposed and entirely new equivariant network architectures and extensively compare their performances. $E(2)$-steerable convolutions are further shown to yield remarkable gains on CIFAR-10, CIFAR-100 and STL-10 when used as a drop-in replacement for non-equivariant convolutions.

Includes members:
  Member "spherical cnns on unstructured grids":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "silhonet fisheye adaptation of a roi based object pose estimation network to monocular fisheye images")
  Member "gauge equivariant convolutional networks and the icosahedral cnn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on learning sets of symmetric elements")
  Member "learning to convolve a generalized weight tying approach":
    also connected through other hyperedges:
      (none)
  Member "n body networks a covariant hierarchical neural network architecture for learning atomic potentials":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on learning sets of symmetric elements")
  Member "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "roto translation covariant convolutional networks for medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attentive group equivariant convolutional networks")
  Member "multi directional geodesic neural networks via equivariant convolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "continuous geodesic convolutions for learning on 3d shapes")
  Member "3d g cnns for pulmonary nodule detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on learning sets of symmetric elements")

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.CV(Computer Vision and Pattern Recognition)