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: "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"
  Abstract: We present a method for parallel block-sparse matrix-matrix multiplication.A distributed quadtree matrix representation allows exploitation of data locality.The quadtree structure is implemented using the Chunks and Tasks programming model.Data locality is exploited without prior information about matrix sparsity pattern.Constant communication per node on average is achieved in weak scaling tests. We present a method for parallel block-sparse matrix-matrix multiplication on distributed memory clusters. By using a quadtree matrix representation, data locality is exploited without prior information about the matrix sparsity pattern. A distributed quadtree matrix representation is straightforward to implement due to our recent development of the Chunks and Tasks programming model Parallel Comput. 40, 328 (2014). The quadtree representation combined with the Chunks and Tasks model leads to favorable weak and strong scaling of the communication cost with the number of processes, as shown both theoretically and in numerical experiments.Matrices are represented by sparse quadtrees of chunk objects. The leaves in the hierarchy are block-sparse submatrices. Sparsity is dynamically detected by the matrix library and may occur at any level in the hierarchy and/or within the submatrix leaves. In case graphics processing units (GPUs) are available, both CPUs and GPUs are used for leaf-level multiplication work, thus making use of the full computing capacity of each node.The performance is evaluated for matrices with different sparsity structures, including examples from electronic structure calculations. Compared to methods that do not exploit data locality, our locality-aware approach reduces communication significantly, achieving essentially constant communication per node in weak scaling tests.

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"
    - "a survey of sparse matrix vector multiplication performance on large matrices"

Connected through hyperedge HE-2 (induced by source paper "parallelization and scalability analysis of inverse factorization using the chunks and tasks programming model"):
  with members:
    - "ductteip an efficient programming model for distributed task based parallel computing"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "chunks and tasks a programming model for parallelization of dynamic algorithms"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-3 (induced by source paper "taskuniverse a task based unified interface for versatile parallel execution"):
  with members:
    - "task parallel implementation of a solver for electromagnetic scattering problems"
    - "chunks and tasks a programming model for parallelization of dynamic algorithms"
    - "taking advantage of hybrid systems for sparse direct solvers via task based runtimes"
    - "adaptive fast multipole methods on the gpu"

Connected through hyperedge HE-4 (induced by source paper "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"):
  with members:
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"

Connected through hyperedge HE-5 (induced by source paper "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"):
  with members:
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"

Connected through hyperedge HE-6 (induced by source paper "task based algorithm for matrix multiplication a step towards block sparse tensor computing"):
  with members:
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"

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)