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: "adagcn adaboosting graph convolutional networks into deep models"
  Abstract: The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of network; and the proposed graph convolutional network called AdaGCN~(AdaBoosting Graph Convolutional Network) has the ability to efficiently extract knowledge from high-order neighbors and integrate knowledge from different hops of neighbors into the network in an AdaBoost way. We also present the architectural difference between AdaGCN and existing graph convolutional methods to show the benefits of our proposal. Finally, extensive experiments demonstrate the state-of-the-art prediction performance and the computational advantage of our approach AdaGCN.

Connected through hyperedge HE-1 (induced by source paper "graph neural distance metric learning with graph bert"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "graph bert only attention is needed for learning graph representations"
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "structure fusion based on graph convolutional networks for semi supervised classification"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "wasserstein weisfeiler lehman graph kernels"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "graph matching networks for learning the similarity of graph structured objects"

Connected through hyperedge HE-2 (induced by source paper "get rid of suspended animation problem deep diffusive neural network on graph semi supervised classification"):
  with members:
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep loopy neural network model for graph structured data representation learning"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous deep graph infomax"):
  with members:
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "social network fusion and mining a survey"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "adaptive sampling towards fast graph representation learning"
    - "how powerful are graph neural networks"
    - "mine mutual information neural estimation"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "representation learning on graphs methods and applications"

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.LG(Machine Learning)