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 "benchmarking the graphulo processing framework"):
  Abstract: Graph algorithms have wide applicablity to a variety of domains and are often used on massive datasets. Recent standardization efforts such as the GraphBLAS specify a set of key computational kernels that hardware and software developers can adhere to. Graphulo is a processing framework that enables GraphBLAS kernels in the Apache Accumulo database. In our previous work, we have demonstrated a core Graphulo operation called TableMult that performs large-scale multiplication operations of database tables. In this article, we present the results of scaling the Graphulo engine to larger problems and scalablity when a greater number of resources is used. Specifically, we present two experiments that demonstrate Graphulo scaling performance is linear with the number of available resources. The first experiment demonstrates cluster processing rates through Graphulo's TableMult operator on two large graphs, scaled between 217 and 219 vertices. The second experiment uses TableMult to extract a random set of rows from a large graph (219 nodes) to simulate a cued graph analytic. These benchmarking results are of relevance to Graphulo users who wish to apply Graphulo to their graph problems.

Includes members:
  Member "using a power law distribution to describe big data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ai enabling technologies a survey")
  Member "associative arrays unified mathematics for spreadsheets databases matrices and graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database")
  Member "graphs matrices and the graphblas seven good reasons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automatically harnessing sparse acceleration")
  Member "graphulo implementation of server side sparse matrix multiply in the accumulo database":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "streaming 1 9 billion hypersparse network updates per second with d4m")
  Member "graphulo linear algebra graph kernels for nosql databases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "achieving 100 000 000 database inserts per second using accumulo and d4m":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "75 000 000 000 streaming inserts second using hierarchical hypersparse graphblas matrices")
  Member "big data dimensional analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ai enabling technologies a survey")
  Member "standards for graph algorithm primitives":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")

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)