human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "d4m 3 0 extended database and language capabilities"):
  with members:
    - "the bigdawg polystore system and architecture"
    - "julia implementation of the dynamic distributed dimensional data model"
    - "benchmarking scidb data import on hpc systems"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "using a power law distribution to describe big data"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "enabling on demand database computing with mit supercloud database management system"
    - "graphulo linear algebra graph kernels for nosql databases"

Connected through hyperedge HE-2 (induced by source paper "distributed triangle counting in the graphulo matrix math library"):
  with members:
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "static graph challenge subgraph isomorphism"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "pipegen data pipe generator for hybrid analytics"
    - "the bigdawg polystore system and architecture"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "using a power law distribution to describe big data"

Connected through hyperedge HE-3 (induced by source paper "d4m 3 0"):
  with members:
    - "julia implementation of the dynamic distributed dimensional data model"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "computing on masked data to improve the security of big data"

Question: Please tell me which class the center node 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)