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: "highly parallel sparse matrix matrix multiplication"
  Abstract: Generalized sparse matrix-matrix multiplication is a key primitive for many high performance graph algorithms as well as some linear solvers such as multigrid. We present the first parallel algorithms that achieve increasing speed ups for an unbounded number of processors. Our algorithms are based on two-dimensional block distribution of sparse matrices where serial sections use a novel hypersparse kernel for scalability. We give a state-of-the-art MPI implementation of one of our algorithms. Our experiments show scaling up to thousands of processors on a variety of test scenarios.

Connected through hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication"):
  with members:
    - "high performance sparse matrix matrix products on intel knl and multicore architectures"
    - "sparse matrix matrix multiplication on multilevel memory architectures algorithms and experiments"
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"
    - "static graph challenge subgraph isomorphism"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"

Connected through hyperedge HE-2 (induced by source paper "a 2d parallel triangle counting algorithm for distributed memory architectures"):
  with members:
    - "distributed memory parallel algorithms for counting and listing triangles in big graphs"
    - "static graph challenge subgraph isomorphism"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "counting triangles in large graphs on gpu"

Connected through hyperedge HE-3 (induced by source paper "properties of healthcare teaming networks as a function of network construction algorithms"):
  with members:
    - "navigating the massive world of reddit using backbone networks to map user interests in social media"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-4 (induced by source paper "graphulo implementation of server side sparse matrix multiply in the accumulo database"):
  with members:
    - "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"
    - "standards for graph algorithm primitives"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-5 (induced by source paper "solvers for mathcal o n electronic structure in the strong scaling limit"):
  with members:
    - "communication an n body solution to the problem of fock exchange"
    - "faster gaussian summation theory and experiment"
    - "an optimized sparse approximate matrix multiply for matrices with decay"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "fast multiplication of matrices with decay"

Connected through hyperedge HE-6 (induced by source paper "parallel sparse matrix matrix multiplication and indexing implementation and experiments"):
  with members:
    - "minimizing communication in linear algebra"
    - "parallel breadth first search on distributed memory systems"

Connected through hyperedge HE-7 (induced by source paper "the similarity between stochastic kronecker and chung lu graph models"):
  with members:
    - "an in depth analysis of stochastic kronecker graphs"
    - "multiplicative attribute graph model of real world networks"

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)