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: "processing sparql queries over distributed rdf graphs"
  Abstract: We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a "partial evaluation and assembly" framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system's performance and scalability.

Connected through hyperedge HE-1 (induced by source paper "adaptive low level storage of very large knowledge graphs"):
  with members:
    - "a survey of rdf data management systems"
    - "snap a general purpose network analysis and graph mining library"
    - "kognac efficient encoding of large knowledge graphs"
    - "taming subgraph isomorphism for rdf query processing"
    - "ringo interactive graph analytics on big memory machines"
    - "s2rdf rdf querying with sparql on spark"

Connected through hyperedge HE-2 (induced by source paper "semantic guided and response times bounded top k similarity search over knowledge graphs"):
  with members:
    - "accelerating partial evaluation in distributed sparql query evaluation"
    - "keyword search on rdf graphs a query graph assembly approach"
    - "an analytical study of large sparql query logs"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "querying knowledge graphs by example entity tuples"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-3 (induced by source paper "accelerating partial evaluation in distributed sparql query evaluation"):
  with members:
    - "query workload based rdf graph fragmentation and allocation"
    - "s2rdf rdf querying with sparql on spark"
    - "performance guarantees for distributed reachability queries"

Connected through hyperedge HE-4 (induced by source paper "piwd a plugin based framework for well designed sparql"):
  with members:
    - "on the statistical analysis of practical sparql queries"
    - "efficient approximation of well designed sparql queries"
    - "left bit right for sparql join queries with optional patterns left outer joins"
    - "context free path queries on rdf graphs"
    - "on the satisfiability problem for sparql patterns"
    - "efficient subgraph matching on billion node graphs"
    - "foundations of sparql query optimization"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-5 (induced by source paper "a survey of rdf data management systems"):
  with members:
    - "clustering rdf databases using tunable lsh"
    - "taming subgraph isomorphism for rdf query processing"
    - "stochastic database cracking towards robust adaptive indexing in main memory column stores"
    - "partout a distributed engine for efficient rdf processing"

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)