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 "approximate evaluation of label constrained reachability queries"):
  Abstract: The current surge of interest in graph-based data models mirrors the usage of increasingly complex reachability queries, as witnessed by recent analytical studies on real-world graph query logs. Despite the maturity of graph DBMS capabilities, complex label-constrained reachability queries, along with their corresponding aggregate versions, remain difficult to evaluate. In this paper, we focus on the approximate evaluation of counting label-constrained reachability queries. We offer a human-explainable solution to graph Approximate Query Processing (AQP). This consists of a summarization algorithm (GRASP), as well as of a custom visualization plug-in, which allows users to explore the obtained summaries. We prove that the problem of node group minimization, associated to the creation of GRASP summaries, is NP-complete. Nonetheless, our GRASP summaries are reasonably small in practice, even for large graph instances, and guarantee approximate graph query answering, paired with controllable error estimates. We experimentally gauge the scalability and efficiency of our GRASP algorithm, and verify the accuracy and error estimation of the graph AQP module. To the best of our knowledge, ours is the first system capable of handling visualization-driven approximate graph analytics for complex label-constrained reachability queries.

Includes members:
  Member "estimating the cardinality of conjunctive queries over rdf data using graph summarisation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "functorial question answering")
  Member "gmark schema driven generation of graphs and queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph generators state of the art and open challenges")
  Member "g core a core for future graph query languages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph generators state of the art and open challenges")
  Member "an analytical study of large sparql query logs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge graphs")
  Member "the ubiquity of large graphs and surprising challenges of graph processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries")
  Member "foundations of modern query languages for graph databases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "graph summarization methods and applications a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "knowledge graphs")
  Member "blinkdb queries with bounded errors and bounded response times on very large data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ml aqp query driven approximate query processing based on machine learning")

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)