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: "speculative segmented sum for sparse matrix vector multiplication on heterogeneous processors"
  Abstract: A speculative segmented sum strategy for the CSR-based SpMV.Utilizing both GPU cores and CPU cores in a heterogeneous processor.No format conversion or tuning overhead for input sparse matrices in the CSR format.High speedup over the CSR-vector algorithm running irregular matrices.No performance penalty for most regular matrices. Sparse matrix-vector multiplication (SpMV) is a central building block for scientific software and graph applications. Recently, heterogeneous processors composed of different types of cores attracted much attention because of their flexible core configuration and high energy efficiency. In this paper, we propose a compressed sparse row (CSR) format based SpMV algorithm utilizing both types of cores in a CPU-GPU heterogeneous processor. We first speculatively execute segmented sum operations on the GPU part of a heterogeneous processor and generate a possibly incorrect result. Then the CPU part of the same chip is triggered to re-arrange the predicted partial sums for a correct resulting vector. On three heterogeneous processors from Intel, AMD and nVidia, using 20 sparse matrices as a benchmark suite, the experimental results show that our method obtains significant performance improvement over the best existing CSR-based SpMV algorithms.

Connected through hyperedge HE-1 (induced by source paper "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"):
  with members:
    - "optimizing deep learning inference on embedded systems through adaptive model selection"
    - "machine learning in compiler optimisation"
    - "improving spark application throughput via memory aware task co location a mixture of experts approach"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"

Connected through hyperedge HE-2 (induced by source paper "a recursive algebraic coloring technique for hardware efficient symmetric sparse matrix vector multiplication"):
  with members:
    - "chebyshev filter diagonalization on modern manycore processors and gpgpus"
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"):
  with members:
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
    - "the input output complexity of sparse matrix multiplication"
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "better size estimation for sparse matrix products"
    - "finding heaviest h subgraphs in real weighted graphs with applications"

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.MS(Mathematical Software)