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 "one size does not fit all when to use signature based pruning to improve template matching for rdf graphs"):
  Abstract: Signature-based pruning is broadly accepted as an effective way to improve query performance of graph template matching on general labeled graphs. Most existing techniques which utilize signature-based pruning claim its benefits on all datasets and queries. However, the effectiveness of signature-based pruning varies greatly among different RDF datasets and highly related with their dataset characteristics. We observe that the performance benefits from signature-based pruning depend not only on the size of the RDF graphs, but also the underlying graph structure and the complexity of queries. This motivates us to propose a flexible RDF querying framework, called RDF-h, which selectively utilizes signature-based pruning by evaluating the characteristics of RDF datasets and query templates. Scalability and efficiency of RDF-h is demonstrated in experimental results using both real and synthetic datasets. #R##N#Keywords: RDF, Graph Template Matching, Signature-based Pruning

Includes members:
  Member "efficient subgraph matching on billion node graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast detection of maximum common subgraph via deep q learning")
  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")
  Member "sp2bench a sparql performance benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards semantic big graph analytics for cross domain knowledge discovery")

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)