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: "group equivariant capsule networks"
  Abstract: We present group equivariant capsule networks, a framework to introduce guaranteed equivariance and invariance properties to the capsule network idea. Our work can be divided into two contributions. First, we present a generic routing by agreement algorithm defined on elements of a group and prove that equivariance of output pose vectors, as well as invariance of output activations, hold under certain conditions. Second, we connect the resulting equivariant capsule networks with work from the field of group convolutional networks. Through this connection, we provide intuitions of how both methods relate and are able to combine the strengths of both approaches in one deep neural network architecture. The resulting framework allows sparse evaluation of the group convolution operator, provides control over specific equivariance and invariance properties, and can use routing by agreement instead of pooling operations. In addition, it is able to provide interpretable and equivariant representation vectors as output capsules, which disentangle evidence of object existence from its pose.

Connected through hyperedge HE-1 (induced by source paper "attentive group equivariant convolutional networks"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "general e 2 equivariant steerable cnns"
    - "building deep equivariant capsule networks"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "stand alone self attention in vision models"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "attention augmented convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds"):
  with members:
    - "equivariant multi view networks"
    - "spherical cnns on unstructured grids"
    - "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning an effective equivariant 3d descriptor without supervision"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "equivariant neural networks and equivarification"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-3 (induced by source paper "graphter unsupervised learning of graph transformation equivariant representations via auto encoding node wise transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"
    - "learning generalized transformation equivariant representations via autoencoding transformations"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "relation shape convolutional neural network for point cloud analysis"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-4 (induced by source paper "b spline cnns on lie groups"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "scale aware trident networks for object detection"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "3d g cnns for pulmonary nodule detection"
    - "template matching via densities on the roto translation group"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"

Connected through hyperedge HE-5 (induced by source paper "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "learning generalized transformation equivariant representations via autoencoding transformations"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "intertwiners between induced representations with applications to the theory of equivariant neural networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "dynamic routing between capsules"
    - "scaling the scattering transform deep hybrid networks"

Connected through hyperedge HE-6 (induced by source paper "transformations are all you need learning generalized transformation equivariant representations via autoencoding transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "dynamic routing between capsules"
    - "scaling the scattering transform deep hybrid networks"
    - "representation learning by learning to count"

Connected through hyperedge HE-7 (induced by source paper "learning generalized transformation equivariant representations via autoencoding transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "dynamic routing between capsules"
    - "scaling the scattering transform deep hybrid networks"
    - "representation learning by learning to count"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-8 (induced by source paper "a neural symbolic architecture for inverse graphics improved by lifelong meta learning"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "3d point capsule networks"
    - "non stationary texture synthesis by adversarial expansion"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "3d aware scene manipulation via inverse graphics"
    - "methods for interpreting and understanding deep neural networks"
    - "physical primitive decomposition"

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.CV(Computer Vision and Pattern Recognition)