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 "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"):
  Abstract: Google BigTable's scale-out design for distributed key-value storage inspired a generation of NoSQL databases. Recently the NewSQL paradigm emerged in response to analytic workloads that demand distributed computation local to data storage. Many such analytics take the form of graph algorithms, a trend that motivated the GraphBLAS initiative to standardize a set of matrix math kernels for building graph algorithms. In this article we show how it is possible to implement the GraphBLAS kernels in a BigTable database by presenting the design of Graphulo, a library for executing graph algorithms inside the Apache Accumulo database. We detail the Graphulo implementation of two graph algorithms and conduct experiments comparing their performance to two main-memory matrix math systems. Our results shed insight into the conditions that determine when executing a graph algorithm is faster inside a database versus an external system—in short, that memory requirements and relative I/O are critical factors.

Includes members:
  Member "pipegen data pipe generator for hybrid analytics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polystore accelerated polystore system for heterogeneous workloads")
  Member "using a power law distribution to describe big data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "benchmarking the graphulo processing framework")
  Member "graphs matrices and the graphblas seven good reasons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (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-7 (induced by source paper "75 000 000 000 streaming inserts second using hierarchical hypersparse graphblas matrices")
  Member "d4m 2 0 schema a general purpose high performance schema for the accumulo database":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "securing hpc using federated authentication")

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)