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: "old techniques for new join algorithms a case study in rdf processing"
  Abstract: Recently there has been significant interest around designing specialized RDF engines, as traditional query processing mechanisms incur orders of magnitude performance gaps on many RDF workloads. At the same time researchers have released new worst-case optimal join algorithms which can be asymptotically better than the join algorithms in traditional engines. In this paper we apply worst-case optimal join algorithms to a standard RDF workload, the LUBM benchmark, for the first time. We do so using two worst-case optimal engines: (1) LogicBlox, a commercial database engine, and (2) EmptyHeaded, our prototype research engine with enhanced worst-case optimal join algorithms. We show that without any added optimizations both LogicBlox and EmptyHeaded outperform two state-of-the-art specialized RDF engines, RDF-3X and TripleBit, by up to 6x on cyclic join queries-the queries where traditional optimizers are suboptimal. On the remaining, less complex queries in the LUBM benchmark, we show that three classic query optimization techniques enable EmptyHeaded to compete with RDF engines, even when there is no asymptotic advantage to the worst-case optimal approach. We validate that our design has merit as EmptyHeaded outperforms MonetDB by three orders of magnitude and LogicBlox by two orders of magnitude, while remaining within an order of magnitude of RDF-3X and TripleBit.

Connected through hyperedge HE-1 (induced by source paper "complexity analysis of general and fractional hypertree decompositions"):
  with members:
    - "hyperbench a benchmark and tool for hypergraphs and empirical findings"
    - "tree projections and constraint optimization problems fixed parameter tractability and parallel algorithms"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "myhill nerode methods for hypergraphs"
    - "faq questions asked frequently"

Connected through hyperedge HE-2 (induced by source paper "hyperbench a benchmark and tool for hypergraphs and empirical findings"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "xcsp3 an integrated format for benchmarking combinatorial constrained problems"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"
    - "aggregation and ordering in factorised databases"

Connected through hyperedge HE-3 (induced by source paper "an analytical study of large sparql query logs"):
  with members:
    - "gmark schema driven generation of graphs and queries"
    - "enumeration problems for regular path queries"
    - "on the statistical analysis of practical sparql queries"
    - "flexible caching in trie joins"
    - "a trichotomy for regular simple path queries on graphs"
    - "an empirical study of real world sparql queries"
    - "semantics and complexity of sparql"
    - "hypertree decompositions and tractable queries"

Connected through hyperedge HE-4 (induced by source paper "levelheaded making worst case optimal joins work in the common case"):
  with members:
    - "mllib machine learning in apache spark"
    - "leapfrog triejoin a worst case optimal join algorithm"

Connected through hyperedge HE-5 (induced by source paper "general and fractional hypertree decompositions hard and easy cases"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "an analytical study of large sparql query logs"
    - "myhill nerode methods for hypergraphs"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"
    - "gym a multiround join algorithm in mapreduce"
    - "aggregation and ordering in factorised databases"

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)