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: "taming subgraph isomorphism for rdf query processing"
  Abstract: RDF data are used to model knowledge in various areas such as life sciences, Semantic Web, bioinformatics, and social graphs. The size of real RDF data reaches billions of triples. This calls for a framework for efficiently processing RDF data. The core function of processing RDF data is subgraph pattern matching. There have been two completely different directions for supporting efficient subgraph pattern matching. One direction is to develop specialized RDF query processing engines exploiting the properties of RDF data for the last decade, while the other direction is to develop efficient subgraph isomorphism algorithms for general, labeled graphs for over 30 years. Although both directions have a similar goal (i.e., finding subgraphs in data graphs for a given query graph), they have been independently researched without clear reason. We argue that a subgraph isomorphism algorithm can be easily modified to handle the graph homomorphism, which is the RDF pattern matching semantics, by just removing the injectivity constraint. In this paper, based on the state-of-the-art subgraph isomorphism algorithm, we propose an in-memory solution, TurboHOM++, which is tamed for the RDF processing, and we compare it with the representative RDF processing engines for several RDF benchmarks in a server machine where billions of triples can be loaded in memory. In order to speed up TurboHOM++, we also provide a simple yet effective transformation and a series of optimization techniques. Extensive experiments using several RDF benchmarks show that TurboHOM++ consistently and significantly outperforms the representative RDF engines. Specifically, TurboHOM++ outperforms its competitors by up to five orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "structural indexing for conjunctive path queries"):
  with members:
    - "the ubiquity of large graphs and surprising challenges of graph processing extended survey"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "gmark schema driven generation of graphs and queries"
    - "g core a core for future graph query languages"
    - "an analytical study of large sparql query logs"
    - "foundations of modern query languages for graph databases"
    - "similarity and bisimilarity notions appropriate for characterizing indistinguishability in fragments of the calculus of relations"
    - "relative expressive power of navigational querying on graphs"

Connected through hyperedge HE-2 (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"
    - "ringo interactive graph analytics on big memory machines"
    - "s2rdf rdf querying with sparql on spark"
    - "processing sparql queries over distributed rdf graphs"

Connected through hyperedge HE-3 (induced by source paper "gsi gpu friendly subgraph isomorphism"):
  with members:
    - "optimizing subgraph queries by combining binary and worst case optimal joins"
    - "gunrock a high performance graph processing library on the gpu"
    - "efficient subgraph matching on billion node graphs"
    - "a new data layout for set intersection on gpus"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-4 (induced by source paper "learning to speed up query planning in graph databases"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-5 (induced by source paper "gsmat a scalable sparse matrix based join for sparql query processing"):
  with members:
    - "tripleid q rdf query processing framework using gpu"
    - "foundations of sparql query optimization"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-6 (induced by source paper "a survey of rdf data management systems"):
  with members:
    - "clustering rdf databases using tunable lsh"
    - "processing sparql queries over distributed rdf graphs"
    - "stochastic database cracking towards robust adaptive indexing in main memory column stores"
    - "partout a distributed engine for efficient rdf processing"

Connected through hyperedge HE-7 (induced by source paper "old techniques for new join algorithms a case study in rdf processing"):
  with members:
    - "size bounds and query plans for relational joins"
    - "emptyheaded a relational engine for graph processing"
    - "aggregations over generalized hypertree decompositions"

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)