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 "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.

Includes members:
  Member "hypergraph partitioning for sparse matrix matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "exploiting multiple levels of parallelism in sparse matrix matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "chunks and tasks a programming model for parallelization of dynamic algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "an optimized sparse approximate matrix multiply for matrices with decay":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithms and data structures for matrix free finite element operators with mpi parallel sparse multi vectors")
  Member "parallel sparse matrix matrix multiplication and indexing implementation and experiments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the parallelism motifs of genomic data analysis")

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.DC(Distributed, Parallel, and Cluster Computing)