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: "graph optimized convolutional networks"
  Abstract: Graph Convolutional Networks (GCNs) have been widely studied for graph data representation and learning tasks. Existing GCNs generally use a fixed single graph which may lead to weak suboptimal for data representation/learning and are also hard to deal with multiple graphs. To address these issues, we propose a novel Graph Optimized Convolutional Network (GOCN) for graph data representation and learning. Our GOCN is motivated based on our re-interpretation of graph convolution from a regularization/optimization framework. The core idea of GOCN is to formulate graph optimization and graph convolutional representation into a unified framework and thus conducts both of them cooperatively to boost their respective performance in GCN learning scheme. Moreover, based on the proposed unified graph optimization-convolution framework, we propose a novel Multiple Graph Optimized Convolutional Network (M-GOCN) to naturally address the data with multiple graphs. Experimental results demonstrate the effectiveness and benefit of the proposed GOCN and M-GOCN.

Connected through hyperedge HE-1 (induced by source paper "gresnet graph residual network for reviving deep gnns from suspended animation"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "graph neural networks a review of methods and applications"
    - "norm preservation why residual networks can become extremely deep"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "invertible residual networks"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"

Connected through hyperedge HE-2 (induced by source paper "gmcn graph mask convolutional network"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "adaptive graph convolutional neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-3 (induced by source paper "semi supervised learning with adaptive neighborhood graph propagation network"):
  with members:
    - "graph mask convolutional network"
    - "adaptive graph convolutional neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"
    - "adam a method for stochastic optimization"

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)