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: "a technique for sparql optional left outer join queries"
  Abstract: SPARQL basic graph pattern (BGP) (aka SQL inner-join) query optimization is a well researched area. But optimization of OPTIONAL pattern queries (SQL left-outer-joins) poses additional challenges, because of the restriction on the reorderability of left-joins. Query logs of SPARQL endpoints like that of DBPedia show that the occurrence of OPTIONAL pattern queries is as high as 17-50%. Previously published work on SQL left-outer-joins proposes free-reorderability of left-outer-joins, using additional operators like nullification and best-match to remove spurious (incorrect) results. #R##N#In this paper, we present a novel technique - OptBitMat - for nested SPARQL OPTIONAL and BGP queries. first constructs and manipulates a query graph of hypernodes. Further OptBitMat's 2-phase query processing algorithm uses this query graph to (i) aggressively prune the candidate RDF triples, and (ii) generate final results without pairwise joins, unlike the traditional join query processors. OptBitMat keeps the memory footprint of the query processor low by using compressed bit-matrices (BitMats) to represent an RDF graph, works on them without uncompressing, and does not generate spurious intermediate results. We have demonstrated OptBitMat's superior performance in comparison to MonetDB and IBM DB2 using nested SPARQL BGP and OPTIONAL pattern queries of varying complexity and selectivity, over RDF graphs of up to 1.33 billion triples (much larger than the previously published work on left-outer-joins), using a commodity Linux machine.

Connected through hyperedge HE-1 (induced by source paper "join size estimation on boolean tensors of rdf data"):
  with members:
    - "fdb a query engine for factorised relational databases"
    - "better size estimation for sparse matrix products"

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)