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: "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
  Abstract: Intel Xeon Phi is a recently released high-performance coprocessor which features 61 cores each supporting 4 hardware threads with 512-bit wide SIMD registers achieving a peak theoretical performance of 1Tflop/s in double precision. Many scientific applications involve operations on large sparse matrices such as linear solvers, eigensolver, and graph mining algorithms. The core of most of these applications involves the multiplication of a large, sparse matrix with a dense vector (SpMV). In this paper, we investigate the performance of the Xeon Phi coprocessor for SpMV. We first provide a comprehensive introduction to this new architecture and analyze its peak performance with a number of micro benchmarks. Although the design of a Xeon Phi core is not much different than those of the cores in modern processors, its large number of cores and hyperthreading capability allow many application to saturate the available memory bandwidth, which is not the case for many cutting-edge processors. Yet, our performance studies show that it is the memory latency not the bandwidth which creates a bottleneck for SpMV on this architecture. Finally, our experiments show that Xeon Phi's sparse kernel performance is very promising and even better than that of cutting-edge general purpose processors and GPUs.

Connected through hyperedge HE-1 (induced by source paper "prins resistive cam processing in storage"):
  with members:
    - "sparse matrix multiplication on an associative processor"
    - "application driven near data processing for similarity search"
    - "computer architecture with associative processor replacing last level cache and simd accelerator"

Connected through hyperedge HE-2 (induced by source paper "sparse matrix multiplication on cam based accelerator"):
  with members:
    - "sparse matrix multiplication on an associative processor"
    - "computer architecture with associative processor replacing last level cache and simd accelerator"

Connected through hyperedge HE-3 (induced by source paper "sparse matrix multiplication on an associative processor"):
  with members:
    - "computer architecture with associative processor replacing last level cache and simd accelerator"

Connected through hyperedge HE-4 (induced by source paper "dot product join an array relation join operator for big model analytics"):
  with members:
    - "mli an api for distributed machine learning"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"
    - "a reliable effective terascale linear learning system"
    - "fast sparse matrix vector multiplication on gpus implications for graph mining"

Connected through hyperedge HE-5 (induced by source paper "finite element numerical integration for first order approximations on multi and many core architectures"):
  with members:
    - "cross loop optimization of arithmetic intensity for finite element local assembly"
    - "numerical integration on gpus for higher order finite elements"
    - "finite element integration on gpus"
    - "vectorized opencl implementation of numerical integration for higher order finite elements"
    - "dolfin automated finite element computing"

Connected through hyperedge HE-6 (induced by source paper "field programmable crossbar array fpca for reconfigurable computing"):
  with members:
    - "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "performance engineering of the kernel polynomal method on large scale cpu gpu systems"

Connected through hyperedge HE-7 (induced by source paper "pipelined iterative solvers with kernel fusion for graphics processing units"):
  with members:
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"
    - "biomolecular electrostatics using a fast multipole bem on up to 512 gpus and a billion unknowns"
    - "a performance comparison of cuda and opencl"

Connected through hyperedge HE-8 (induced by source paper "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"):
  with members:
    - "speculative segmented sum for sparse matrix vector multiplication on heterogeneous processors"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
    - "the input output complexity of sparse matrix multiplication"
    - "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.PF(Performance)