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 "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"):
  Abstract: General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method (AMG), breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM implementation has to handle extra irregularity from three aspects: (1) the number of nonzero entries in the resulting sparse matrix is unknown in advance, (2) very expensive parallel insert operations at random positions in the resulting sparse matrix dominate the execution time, and (3) load balancing must account for sparse data in both input matrices.In this work we propose a framework for SpGEMM on GPUs and emerging CPU-GPU heterogeneous processors. This framework particularly focuses on the above three problems. Memory pre-allocation for the resulting matrix is organized by a hybrid method that saves a large amount of global memory space and efficiently utilizes the very limited on-chip scratchpad memory. Parallel insert operations of the nonzero entries are implemented through the GPU merge path algorithm that is experimentally found to be the fastest GPU merge approach. Load balancing builds on the number of necessary arithmetic operations on the nonzero entries and is guaranteed in all stages.Compared with the state-of-the-art CPU and GPU SpGEMM methods, our approach delivers excellent absolute performance and relative speedups on various benchmarks multiplying matrices with diverse sparsity structures. Furthermore, on heterogeneous processors, our SpGEMM approach achieves higher throughput by using re-allocatable shared virtual memory. We design a framework for SpGEMM on modern manycore processors using the CSR format.We present a hybrid method for pre-allocating the resulting sparse matrix.We propose an efficient parallel insert method for long rows of the resulting matrix.We develop a heuristic-based load balancing strategy.Our approach significantly outperforms other known CPU and GPU SpGEMM methods.

Includes members:
  Member "speculative segmented sum for sparse matrix vector multiplication on heterogeneous processors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000")
  Member "csr5 an efficient storage format for cross platform sparse matrix vector multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a unified iteration space transformation framework for sparse and dense tensor algebra")
  Member "the input output complexity of sparse matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "enabling distributed memory tensor completion in python using new sparse tensor kernels")
  Member "performance evaluation of sparse matrix multiplication kernels on intel xeon phi":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prins resistive cam processing in storage")
  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")
  Member "better size estimation for sparse matrix products":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "finding heaviest h subgraphs in real weighted graphs with applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")

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.MS(Mathematical Software)