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 "accelerating partial evaluation in distributed sparql query evaluation"):
  Abstract: Partial evaluation has recently been used for processing SPARQL queries over a large resource description framework (RDF) graph in a distributed environment. However, the previous approach is inefficient when dealing with complex queries. In this study, we further improve the "partial evaluation and assembly" framework for answering SPARQL queries over a distributed RDF graph, while providing performance guarantees. Our key idea is to explore the intrinsic structural characteristics of partial matches to filter out irrelevant partial results, while providing performance guarantees on a network trace (data shipment) or the computational cost (response time). We also propose an efficient assembly algorithm to utilize the characteristics of partial matches to merge them and form final results. To improve the efficiency of finding partial matches further, we propose an optimization that communicates variables' candidates among sites to avoid redundant computations. In addition, although our approach is partitioning-tolerant, different partitioning strategies result in different performances, and we evaluate different partitioning strategies for our approach. Experiments over both real and synthetic RDF datasets confirm the superiority of our approach.

Includes members:
  Member "query workload based rdf graph fragmentation and allocation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "loom query aware partitioning of online graphs")
  Member "s2rdf rdf querying with sparql on spark":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive low level storage of very large knowledge graphs")
  Member "processing sparql queries over distributed rdf graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive low level storage of very large knowledge graphs")
  Member "performance guarantees for distributed reachability queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "processing sparql queries over distributed rdf graphs")

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.DC(Distributed, Parallel, and Cluster Computing)