human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "killing two birds with one stone querying property graphs using sparql via gremlinator":
    also connected through other hyperedges:
      (none)
  Member "the multiset semantics of sparql patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards an integrated graph algebra for graph pattern matching with gremlin extended version")
  Member "a formal graph model for rdf and its implementation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "direct mappings between rdf and property graph databases")
  Member "the gremlin graph traversal machine and language invited talk":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "knowledge graphs")
  Member "the graph traversal pattern":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards an integrated graph algebra for graph pattern matching with gremlin extended version")
  Member "foundations of sparql query optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards an integrated graph algebra for graph pattern matching with gremlin extended version")
  Member "sp2bench a sparql performance benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards semantic big graph analytics for cross domain knowledge discovery")
  Member "semantics and complexity of sparql":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "functional programming in pattern match oriented programming style")

Question: Please tell me which class the source paper 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)