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: "gmine a system for scalable interactive graph visualization and mining"
  Abstract: Several graph visualization tools exist. However, they are not able to handle large graphs, and/or they do not allow interaction. We are interested on large graphs, with hundreds of thousands of nodes. Such graphs bring two challenges: the first one is that any straightforward interactive manipulation will be prohibitively slow. The second one is sensory overload: even if we could plot and replot the graph quickly, the user would be overwhelmed with the vast volume of information because the screen would be too cluttered as nodes and edges overlap each other. GMine system addresses both these issues, by using summarization and multi-resolution. GMine offers multi-resolution graph exploration by partitioning a given graph into a hierarchy of com-munities-within-communities and storing it into a novel R-tree-like structure which we name G-Tree. GMine offers summarization by implementing an innovative subgraph extraction algorithm and then visualizing its output.

Connected through hyperedge HE-1 (induced by source paper "expanding label sets for graph convolutional networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "semi supervised classification with graph convolutional networks"
    - "diverse landmark sampling from determinantal point processes for scalable manifold learning"

Connected through hyperedge HE-2 (induced by source paper "recs robust graph embedding using connection subgraphs"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "don t walk skip online learning of multi scale network embeddings"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "deltacon a principled massive graph similarity function"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "exploration and visualization in the web of big linked data a survey of the state of the art"):
  with members:
    - "graphvizdb a scalable platform for interactive large graph visualization"
    - "towards scalable visual exploration of very large rdf graphs"
    - "visualization aware sampling for very large databases"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "rdf synopsviz a framework for hierarchical linked data visual exploration and analysis"
    - "large graph analysis in the gmine system"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-4 (induced by source paper "graphvizdb a scalable platform for interactive large graph visualization"):
  with members:
    - "exploration and visualization in the web of big linked data a survey of the state of the art"
    - "towards scalable visual exploration of very large rdf graphs"

Connected through hyperedge HE-5 (induced by source paper "towards scalable visual exploration of very large rdf graphs"):
  with members:
    - "rdf synopsviz a framework for hierarchical linked data visual exploration and analysis"

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.SI(Social and Information Networks)