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: "coherent comment generation for chinese articles with a graph to sequence model"
  Abstract: Automatic article commenting is helpful in encouraging user engagement and interaction on online news platforms. However, the news documents are usually too long for traditional encoder-decoder based models, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. By organizing the article into graph structure, our model can better understand the internal structure of the article and the connection between topics, which makes it better able to understand the story. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models.

Connected through hyperedge HE-1 (induced by source paper "measuring and relieving the over smoothing problem for graph neural networks from the topological view"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "graph neural networks with convolutional arma filters"
    - "fisher bures adversary graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "hypergraph convolution and hypergraph attention"
    - "just jump dynamic neighborhood aggregation in graph neural networks"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-2 (induced by source paper "improving node classification by co training node pair classification a novel training framework for general graph neural networks"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "graph neural networks with convolutional arma filters"
    - "fast graph representation learning with pytorch geometric"
    - "hypergraph convolution and hypergraph attention"
    - "just jump dynamic neighborhood aggregation in graph neural networks"
    - "demo net degree specific graph neural networks for node and graph classification"

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.CL(Computation and Language)