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 "dynamic discovery of type classes and relations in semantic web data"):
  Abstract: With the rapidly growing resource description framework (RDF) data on the Semantic Web, processing large semantic graph data has become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data based on instance similarities. To scale our approach, we utilized locality-sensitive hashing technique for identifying instance pairs which are candidates to be in the same type class. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.

Includes members:
  Member "an efficient metric of automatic weight generation for properties in instance matching technique":
    also connected through other hyperedges:
      (none)
  Member "axiomatic ranking of network role similarity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "realtime index free single source simrank processing on web scale graphs")
  Member "simrank query rewriting through link analysis of the click graph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain constrained advertising keyword generation")
  Member "a fast general methodology for information theoretically optimal encodings of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations")

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)