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: "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"
  Abstract: Sparse matrix-vector multiplication (spMVM) is the most time-consuming kernel in many numerical algorithms and has been studied extensively on all modern processor and accelerator architectures. However, the optimal sparse matrix data storage format is highly hardware-specific, which could become an obstacle when using heterogeneous systems. Also, it is as yet unclear how the wide single instruction multiple data (SIMD) units in current multi- and many-core processors should be used most efficiently if there is no structure in the sparsity pattern of the matrix. We suggest SELL-$C$-$\sigma$, a variant of Sliced ELLPACK, as a SIMD-friendly data format which combines long-standing ideas from general-purpose graphics processing units and vector computer programming. We discuss the advantages of SELL-$C$-$\sigma$ compared to established formats like Compressed Row Storage and ELLPACK and show its suitability on a variety of hardware platforms (Intel Sandy Bridge, Intel Xeon Phi, and Nvidia Tesla K20) for a wi...

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"
    - "speculative segmented sum for sparse matrix vector multiplication on heterogeneous processors"
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"

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"
    - "speculative segmented sum for sparse matrix vector multiplication on heterogeneous processors"
    - "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 "algorithms and data structures for matrix free finite element operators with mpi parallel sparse multi vectors"):
  with members:
    - "ghost building blocks for high performance sparse linear algebra on heterogeneous systems"
    - "an optimized sparse approximate matrix multiply for matrices with decay"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"

Connected through hyperedge HE-4 (induced by source paper "hierarchical block multi color ordering a new parallel ordering method for vectorization and parallelization of the sparse triangular solver in the iccg method"):
  with members:
    - "the arm scalable vector extension"
    - "implementation and evaluation of data compression algorithms for irregular grid iterative methods on the pezy sc processor"

Connected through hyperedge HE-5 (induced by source paper "chebyshev filter diagonalization on modern manycore processors and gpgpus"):
  with members:
    - "ghost building blocks for high performance sparse linear algebra on heterogeneous systems"
    - "performance engineering of the kernel polynomal method on large scale cpu gpu systems"
    - "efficient estimation of eigenvalue counts in an interval"
    - "sparse matrix vector multiplication on gpgpu clusters a new storage format and a scalable implementation"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"

Connected through hyperedge HE-6 (induced by source paper "computing the sparse matrix vector product using block based kernels without zero padding on processors with avx 512 instructions"):
  with members:
    - "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"

Connected through hyperedge HE-7 (induced by source paper "ghost building blocks for high performance sparse linear algebra on heterogeneous systems"):
  with members:
    - "pipelined iterative solvers with kernel fusion for graphics processing units"
    - "performance engineering of the kernel polynomal method on large scale cpu gpu systems"
    - "asynchronous mpi for the masses"
    - "density matrix based algorithm for solving eigenvalue problems"
    - "pt scotch a tool for efficient parallel graph ordering"

Connected through hyperedge HE-8 (induced by source paper "performance analysis and optimization of sparse matrix vector multiplication on modern multi and many core processors"):
  with members:
    - "csr5 an efficient storage format for cross platform sparse matrix vector 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.MS(Mathematical Software)