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: "capturing topology in graph pattern matching"
  Abstract: Graph pattern matching is often defined in terms of subgraph isomorphism, an NP-complete problem. To lower its complexity, various extensions of graph simulation have been considered instead. These extensions allow pattern matching to be conducted in cubic-time. However, they fall short of capturing the topology of data graphs, i.e., graphs may have a structure drastically different from pattern graphs they match, and the matches found are often too large to understand and analyze. To rectify these problems, this paper proposes a notion of strong simulation, a revision of graph simulation, for graph pattern matching. (1) We identify a set of criteria for preserving the topology of graphs matched. We show that strong simulation preserves the topology of data graphs and finds a bounded number of matches. (2) We show that strong simulation retains the same complexity as earlier extensions of simulation, by providing a cubic-time algorithm for computing strong simulation. (3) We present the locality property of strong simulation, which allows us to effectively conduct pattern matching on distributed graphs. (4) We experimentally verify the effectiveness and efficiency of these algorithms, using real-life data and synthetic data.

Connected through hyperedge HE-1 (induced by source paper "approximate computation for big data analytics"):
  with members:
    - "graph pattern matching for dynamic team formation"
    - "one pass error bounded trajectory simplification"
    - "investigative simulation towards utilizing graph pattern matching for investigative search"
    - "graph based anomaly detection and description a survey"
    - "bounded quadrant system error bounded trajectory compression on the go"
    - "shortest path and distance queries on road networks towards bridging theory and practice"

Connected through hyperedge HE-2 (induced by source paper "cgq relationship aware contextual graph querying in large networks"):
  with members:
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-3 (induced by source paper "big graph search challenges and techniques"):
  with members:
    - "distributed graphlab a framework for machine learning in the cloud"
    - "the big data bootstrap"

Connected through hyperedge HE-4 (induced by source paper "subgraph isomorphism in temporal networks"):
  with members:
    - "efficient subgraph matching on billion node graphs"
    - "a framework for quantitative analysis of cascades on networks"

Connected through hyperedge HE-5 (induced by source paper "one size does not fit all when to use signature based pruning to improve template matching for rdf graphs"):
  with members:
    - "efficient subgraph matching on billion node graphs"
    - "sp2bench a sparql performance benchmark"

Connected through hyperedge HE-6 (induced by source paper "comparison issues in large graphs state of the art and future directions"):
  with members:
    - "practical graph isomorphism ii"

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.DB(Databases)