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: "large graph analysis in the gmine system"
  Abstract: Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers, and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates 1) a representation for graphs organized as hierarchies of partitions-the concepts of SuperGraph and Graph-Tree; and 2) a graph summarization methodology-CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally.

Connected through hyperedge HE-1 (induced by source paper "big data visualization tools"):
  with members:
    - "graphvizdb a scalable platform for interactive large graph visualization"
    - "dinodb an interactive speed query engine for ad hoc queries on temporary data"
    - "exploration and visualization in the web of big linked data a survey of the state of the art"
    - "visualization aware sampling for very large databases"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-2 (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"
    - "gmine a system for scalable interactive graph visualization and mining"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "rdf synopsviz a framework for hierarchical linked data visual exploration and analysis"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-3 (induced by source paper "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"):
  with members:
    - "graphvizdb a scalable platform for interactive large graph visualization"
    - "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"
    - "visualization aware sampling for very large databases"
    - "rapid sampling for visualizations with ordering guarantees"
    - "rdf synopsviz a framework for hierarchical linked data visual exploration and analysis"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

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)