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: "s2rdf rdf querying with sparql on spark"
  Abstract: RDF has become very popular for semantic data publishing due to its flexible and universal graph-like data model. Yet, the ever-increasing size of RDF data collections makes it more and more infeasible to store and process them on a single machine, raising the need for distributed approaches. Instead of building a standalone but closed distributed RDF store, we endorse the usage of existing infrastructures for Big Data processing, e.g. Hadoop. However, SPARQL query performance is a major challenge as these platforms are not designed for RDF processing from ground. Thus, existing Hadoop-based approaches often favor certain query pattern shape while performance drops significantly for other shapes. In this paper, we describe a novel relational partitioning schema for RDF data called ExtVP that uses a semi-join based preprocessing, akin to the concept of Join Indices in relational databases, to efficiently minimize query input size regardless of its pattern shape and diameter. Our prototype system S2RDF is built on top of Spark and uses its relational interface to execute SPARQL queries over ExtVP. We demonstrate its superior performance in comparison to state of the art SPARQL-on-Hadoop approaches using the recent WatDiv test suite. S2RDF achieves sub-second runtimes for majority of queries on a billion triples RDF graph.

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"
    - "processing sparql queries over distributed rdf graphs"

Connected through hyperedge HE-2 (induced by source paper "accelerating partial evaluation in distributed sparql query evaluation"):
  with members:
    - "query workload based rdf graph fragmentation and allocation"
    - "processing sparql queries over distributed rdf graphs"
    - "performance guarantees for distributed reachability queries"

Connected through hyperedge HE-3 (induced by source paper "hierarchical characteristic set merging for optimizing sparql queries in heterogeneous rdf"):
  with members:
    - "the odyssey approach for optimizing federated sparql queries"

Connected through hyperedge HE-4 (induced by source paper "strider a hybrid adaptive distributed rdf stream processing engine"):
  with members:
    - "on measuring performances of c sparql and cqels"

Connected through hyperedge HE-5 (induced by source paper "sparql query processing with apache spark"):
  with members:
    - "skew in parallel query processing"
    - "shark sql and rich analytics at scale"

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)