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: "tensor graph convolutional neural network"
  Abstract: In this paper, we propose a novel tensor graph convolutional neural network (TGCNN) to conduct convolution on factorizable graphs, for which here two types of problems are focused, one is sequential dynamic graphs and the other is cross-attribute graphs. Especially, we propose a graph preserving layer to memorize salient nodes of those factorized subgraphs, i.e. cross graph convolution and graph pooling. For cross graph convolution, a parameterized Kronecker sum operation is proposed to generate a conjunctive adjacency matrix characterizing the relationship between every pair of nodes across two subgraphs. Taking this operation, then general graph convolution may be efficiently performed followed by the composition of small matrices, which thus reduces high memory and computational burden. Encapsuling sequence graphs into a recursive learning, the dynamics of graphs can be efficiently encoded as well as the spatial layout of graphs. To validate the proposed TGCNN, experiments are conducted on skeleton action datasets as well as matrix completion dataset. The experiment results demonstrate that our method can achieve more competitive performance with the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "graph prolongation convolutional networks explicitly multiscale machine learning on graphs with applications to modeling of biological systems"):
  with members:
    - "novel diffusion derived distance measures for graphs"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "a gentle introduction to deep learning for graphs"
    - "hierarchical graph representation learning with differentiable pooling"
    - "multilevel artificial neural network training for spatially correlated learning"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "multigrid neural architectures"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "context dependent diffusion network for visual relationship detection"):
  with members:
    - "action attending graphic neural network"
    - "when work matters transforming classical network structures to graph cnn"
    - "walk steered convolution for graph classification"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "vip cnn visual phrase guided convolutional neural network"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-3 (induced by source paper "gaussian induced convolution for graphs"):
  with members:
    - "context dependent diffusion network for visual relationship detection"
    - "action attending graphic neural network"
    - "when work matters transforming classical network structures to graph cnn"
    - "walk steered convolution for graph classification"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "shift aggregate extract networks"
    - "spectral filter tracking"
    - "learning graph representations with embedding propagation"

Connected through hyperedge HE-4 (induced by source paper "some new layer architectures for graph cnn"):
  with members:
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "learning convolutional neural networks for graphs"
    - "deepwalk online learning of social representations"
    - "spectral networks and locally connected networks on graphs"

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)