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: "graphs matrices and the graphblas seven good reasons"
  Abstract: Abstract   The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard ( istc-bigdata.org/GraphBlas ) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.

Connected through hyperedge HE-1 (induced by source paper "automatically harnessing sparse acceleration"):
  with members:
    - "type directed program synthesis and constraint generation for library portability"
    - "an algorithm for the optimization of finite element integration loops"
    - "polly s polyhedral scheduling in the presence of reductions"
    - "the gap benchmark suite"

Connected through hyperedge HE-2 (induced by source paper "a graphblas approach for subgraph counting"):
  with members:
    - "design principles for sparse matrix multiplication on the gpu"
    - "high performance massive subgraph counting using pipelined adaptive group communication"
    - "accelerating pagerank using partition centric processing"
    - "subgraph counting color coding beyond trees"
    - "the reverse cuthill mckee algorithm in distributed memory"
    - "mathematical foundations of the graphblas"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "ringo interactive graph analytics on big memory machines"

Connected through hyperedge HE-3 (induced by source paper "design generation and validation of extreme scale power law graphs"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "distributed triangle counting in the graphulo matrix math library"
    - "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv"
    - "constructing adjacency arrays from incidence arrays"
    - "an ensemble framework for detecting community changes in dynamic networks"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "static graph challenge subgraph isomorphism"
    - "enabling massive deep neural networks with the graphblas"

Connected through hyperedge HE-4 (induced by source paper "graphchallenge org raising the bar on graph analytic performance"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "distributed triangle counting in the graphulo matrix math library"
    - "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv"
    - "an ensemble framework for detecting community changes in dynamic networks"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "static graph challenge subgraph isomorphism"
    - "enabling massive deep neural networks with the graphblas"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"

Connected through hyperedge HE-5 (induced by source paper "sok cryptographically protected database search"):
  with members:
    - "associative array model of sql nosql and newsql databases"
    - "the bigdawg polystore system and architecture"
    - "pope partial order preserving encoding"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "a practical oblivious map data structure with secure deletion and history independence"
    - "d4m bringing associative arrays to database engines"
    - "computing on masked data to improve the security of big data"

Connected through hyperedge HE-6 (induced by source paper "bigsparse high performance external graph analytics"):
  with members:
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "flashgraph processing billion node graphs on an array of commodity ssds"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-7 (induced by source paper "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"):
  with members:
    - "pipegen data pipe generator for hybrid analytics"
    - "using a power law distribution to describe big data"
    - "associative arrays unified mathematics for spreadsheets databases matrices and graphs"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-8 (induced by source paper "benchmarking the graphulo processing framework"):
  with members:
    - "using a power law distribution to describe big data"
    - "associative arrays unified mathematics for spreadsheets databases matrices and graphs"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "big data dimensional analysis"
    - "standards for graph algorithm primitives"

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.DC(Distributed, Parallel, and Cluster Computing)