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: "towards scalable visual exploration of very large rdf graphs"
  Abstract: In this paper, we outline our work on developing a disk-based infrastructure for efficient visualization and graph exploration operations over very large graphs. The proposed platform, called graphVizdb, is based on a novel technique for indexing and storing the graph. Particularly, the graph layout is indexed with a spatial data structure, i.e., an R-tree, and stored in a database. In runtime, user operations are translated into efficient spatial operations (i.e., window queries) in the backend.

Connected through hyperedge HE-1 (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"
    - "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"
    - "large graph analysis in the gmine system"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-2 (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"
    - "gmine a system for scalable interactive graph visualization and mining"

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"
    - "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"
    - "large graph analysis in the gmine system"
    - "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.HC(Human-Computer Interaction)