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 stitch in time saves nine sparql querying of property graphs using gremlin traversals"
  Abstract: Knowledge graphs have become popular over the past decade and frequently rely on the Resource Description Framework (RDF) or Property Graph (PG) databases as data models. However, the query languages for these two data models -- SPARQL for RDF and the property graph traversal language Gremlin -- are lacking interoperability. We present Gremlinator, a novel SPARQL to Gremlin translator. Gremlinator translates SPARQL queries to Gremlin traversals for executing graph pattern matching queries over graph databases. This allows to access and query a wide variety of Graph Data Management Systems (DMSs) using the W3C standardized SPARQL and avoid the steep learning curve of a new Graph Query Language (GQL). #R##N#Gremlin is a graph computing system agnostic traversal language (covering both OLTP graph database or OLAP graph processors), making it a desirable choice for supporting interoperability for querying Graph DMSs. We present an empirical evaluation for the validity of our approach by formalizing the graph pattern matching construct of Gremlin and illustrate its mapping to corresponding SPARQL queries. Moreover, we also present a proof-of-concept implementation of our approach, demonstrate its validity and applicability by executing SPARQL queries on top of leading Graph stores (Neo4J, Sparksee, and Apache TinkerGraph) and compare their performances with RDF stores (Openlink Virtuoso, 4Store, and JenaTDB). The results indicate that, for complex queries (such as Star-shaped), Gremlin pattern matching traversals out-perform their corresponding SPARQL queries significantly, including their translation time. Gremlinator currently covers a subset of the SPARQL 1.0 specification, specifically the SELECT queries.

Connected through hyperedge HE-1 (induced by source paper "storage solutions for big data systems a qualitative study and comparison"):
  with members:
    - "a billion updates per second using 30 000 hierarchical in memory d4m databases"
    - "foundationdb record layer a multi tenant structured datastore"
    - "hadoop perfect file a fast access container for small files with direct in disc metadata access"
    - "decentralized search on decentralized web"
    - "effects of storage heterogeneity in distributed cache systems"
    - "database as a service current issues and its future"
    - "chain of trust can trusted hardware help scaling blockchains"
    - "towards scaling blockchain systems via sharding"

Connected through hyperedge HE-2 (induced by source paper "bivariate cluster and suitability analysis of nosql solutions for different application areas"):
  with members:
    - "a billion updates per second using 30 000 hierarchical in memory d4m databases"
    - "foundationdb record layer a multi tenant structured datastore"
    - "effects of storage heterogeneity in distributed cache systems"
    - "database as a service current issues and its future"
    - "polypus a big data self deployable architecture for microblogging text extraction and real time sentiment analysis"
    - "blocktag design and applications of a tagging system for blockchain analysis"
    - "g core a core for future graph query languages"
    - "an introduction to graph data management"

Connected through hyperedge HE-3 (induced by source paper "towards an integrated graph algebra for graph pattern matching with gremlin extended version"):
  with members:
    - "the multiset semantics of sparql patterns"
    - "foundations of modern query languages for graph databases"
    - "the gremlin graph traversal machine and language invited talk"
    - "a path algebra for multi relational graphs"
    - "the graph traversal pattern"
    - "foundations of sparql query optimization"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-4 (induced by source paper "direct mappings between rdf and property graph databases"):
  with members:
    - "the query translation landscape a survey"
    - "an introduction to graph data management"
    - "a formal graph model for rdf and its implementation"
    - "foundations of an alternative approach to reification in rdf"

Connected through hyperedge HE-5 (induced by source paper "the query translation landscape a survey"):
  with members:
    - "foundations of modern query languages for graph databases"
    - "the gremlin graph traversal machine and language invited talk"
    - "supporting sparql update queries in rdf xml integration"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-6 (induced by source paper "killing two birds with one stone querying property graphs using sparql via gremlinator"):
  with members:
    - "the gremlin graph traversal machine and language invited talk"
    - "foundations of sparql query optimization"
    - "semantics and complexity of sparql"

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)