human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cgq relationship aware contextual graph querying in large networks"):
  Abstract: The phenomenal growth of graph data from a wide-variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given query graph in a (large) target graph. However, almost all previous research has tacitly ignored the presence of relationships and context (usually manifested in the form of node/edge label distributions) in the query. In this paper, we propose CGQ -- Relationship-Aware Contextual Graph Querying} for real-world graphs. Given a query graph and a target graph, CGQ identifies the (top-k) maximal common subgraphs between the query and the target graphs with the highest contextual similarity. We prove that the problem is NP-hard and APX-Hard. To overcome this computational bottleneck, we propose a hierarchical index, CGQ-Tree, with its associated CGQ search algorithm. Empirically, the CGQ search algorithm is capable of achieving speed-ups of up to three orders of magnitude over a baseline strategy. Our experiments show that CGQ is effective, efficient and scalable.

Includes members:
  Member "deltacon a principled massive graph similarity function":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating descriptors for large graphs")
  Member "capturing topology in graph pattern matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate computation for big data analytics")

Question: Please tell me which class the source paper 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)