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: "sp2bench a sparql performance benchmark"
  Abstract: Recently, the SPARQL query language for RDF has reached the W3C recommendation status. In response to this emerging standard, the database community is currently exploring efficient storage techniques for RDF data and evaluation strategies for SPARQL queries. A meaningful analysis and comparison of these approaches necessitates a comprehensive and universal benchmark platform. To this end, we have developed SP^2Bench, a publicly available, language-specific SPARQL performance benchmark. SP^2Bench is settled in the DBLP scenario and comprises both a data generator for creating arbitrarily large DBLP-like documents and a set of carefully designed benchmark queries. The generated documents mirror key characteristics and social-world distributions encountered in the original DBLP data set, while the queries implement meaningful requests on top of this data, covering a variety of SPARQL operator constellations and RDF access patterns. As a proof of concept, we apply SP^2Bench to existing engines and discuss their strengths and weaknesses that follow immediately from the benchmark results.

Connected through hyperedge HE-1 (induced by source paper "towards semantic big graph analytics for cross domain knowledge discovery"):
  with members:
    - "detection of surgical site infection utilizing automated feature generation in clinical notes"
    - "goffish a sub graph centric framework for large scale graph analytics"

Connected through hyperedge HE-2 (induced by source paper "a stitch in time saves nine sparql querying of property graphs using gremlin traversals"):
  with members:
    - "killing two birds with one stone querying property graphs using sparql via gremlinator"
    - "the multiset semantics of sparql patterns"
    - "a formal graph model for rdf and its implementation"
    - "the gremlin graph traversal machine and language invited talk"
    - "the graph traversal pattern"
    - "foundations of sparql query optimization"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-3 (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"
    - "capturing topology in graph pattern matching"

Connected through hyperedge HE-4 (induced by source paper "gmark controlling workload diversity in benchmarking graph databases"):
  with members:
    - "gmark schema driven generation of graphs and queries"

Connected through hyperedge HE-5 (induced by source paper "querying over federated sparql endpoints a state of the art survey"):
  with members:
    - "an experience report of large scale federations"

Connected through hyperedge HE-6 (induced by source paper "an empirical study of real world sparql queries"):
  with members:
    - "semantics and complexity of sparql"

Connected through hyperedge HE-7 (induced by source paper "foundations of sparql query optimization"):
  with members:
    - "stop the chase"
    - "on chase termination beyond stratification"
    - "a role free approach to indexing large rdf data sets in secondary memory for efficient sparql evaluation"
    - "semantics and complexity of sparql"

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)