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: "exposition and interpretation of the topology of neural networks"
  Abstract: Convolutional neural networks (CNN's) are powerful and widely used tools. However, their interpretability is far from ideal. One such shortcoming is the difficulty of deducing a network's ability to generalize to unseen data. We use topological data analysis to show that the information encoded in the weights of a CNN can be organized in terms of a topological data model and demonstrate how such information can be interpreted and utilized. We show that the weights of convolutional layers at depths from 1 through 13 learn simple global structures. We also demonstrate the change of the simple structures over the course of training. In particular, we define and analyze the spaces of spatial filters of convolutional layers and show the recurrence, among all networks, depths, and during training, of a simple circle consisting of rotating edges, as well as a less recurring unanticipated complex circle that combines lines, edges, and non-linear patterns. We also demonstrate that topological structure correlates with a network's ability to generalize to unseen data and that topological information can be used to improve a network's performance. We train over a thousand CNN's on MNIST, CIFAR-10, SVHN, and ImageNet.

Connected through hyperedge HE-1 (induced by source paper "pi net a deep learning approach to extract topological persistence images"):
  with members:
    - "topology of learning in artificial neural networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "perturbation robust representations of topological persistence diagrams"
    - "quasi periodicity quantification in video data using topology"
    - "deep learning with topological signatures"
    - "persistent homology of attractors for action recognition"
    - "a taxonomy of deep convolutional neural nets for computer vision"
    - "dense optical flow prediction from a static image"

Connected through hyperedge HE-2 (induced by source paper "a topology layer for machine learning"):
  with members:
    - "topology of learning in artificial neural networks"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "adversarial examples target topological holes in deep networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "characterizing the shape of activation space in deep neural networks"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "topology aware surface reconstruction for point clouds"
    - "adversarial attacks and defences a survey"

Connected through hyperedge HE-3 (induced by source paper "towards a topological geometrical theory of group equivariant non expansive operators for data analysis and machine learning"):
  with members:
    - "point convolutional neural networks by extension operators"
    - "dynamic routing between capsules"
    - "harmonic networks deep translation and rotation equivariance"
    - "group equivariant convolutional networks"
    - "on invariance and selectivity in representation learning"
    - "deep learning and the information bottleneck principle"
    - "convolutional neural networks for sentence classification"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "topological approaches to deep learning"):
  with members:
    - "very deep convolutional networks for large scale image recognition"

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)