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: "partout a distributed engine for efficient rdf processing"
  Abstract: The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications with already more than a trillion triples in some cases. Confronted with such huge amounts of data and the future growth, existing state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce Partout, a distributed engine for efficient RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log, allocating the fragments to nodes in a cluster, and finding the optimal configuration. Partout can efficiently handle updates and its query optimizer produces efficient query execution plans for ad-hoc SPARQL queries. Our experiments show the superiority of our approach to state-of-the-art approaches for partitioning and distributed SPARQL query processing.

Connected through hyperedge HE-1 (induced by source paper "business taxonomy construction using concept level hierarchical clustering"):
  with members:
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"

Connected through hyperedge HE-2 (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"
    - "processing sparql queries over distributed rdf graphs"
    - "stochastic database cracking towards robust adaptive indexing in main memory column stores"

Connected through hyperedge HE-3 (induced by source paper "query workload based rdf graph fragmentation and allocation"):
  with members:
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "on the np completeness of some graph cluster measures"

Connected through hyperedge HE-4 (induced by source paper "s2rdf rdf querying with sparql on spark"):
  with members:
    - "semantics and complexity of sparql"

Connected through hyperedge HE-5 (induced by source paper "processing sparql queries over distributed rdf graphs"):
  with members:
    - "left bit right for sparql join queries with optional patterns left outer joins"
    - "performance guarantees for distributed reachability queries"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-6 (induced by source paper "stochastic budget optimization in internet advertising"):
  with members:
    - "budget optimization in search based advertising auctions"
    - "stochastic models for budget optimization in search based advertising"

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)