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: "csr5 an efficient storage format for cross platform sparse matrix vector multiplication"
  Abstract: Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage format, which offers high-throughput SpMV on various platforms including CPUs, GPUs and Xeon Phi. First, the CSR5 format is insensitive to the sparsity structure of the input matrix. Thus the single format can support an SpMV algorithm that is efficient both for regular matrices and for irregular matrices. Furthermore, we show that the overhead of the format conversion from the CSR to the CSR5 can be as low as the cost of a few SpMV operations. We compare the CSR5-based SpMV algorithm with 11 state-of-the-art formats and algorithms on four mainstream processors using 14 regular and 10 irregular matrices as a benchmark suite. For the 14 regular matrices in the suite, we achieve comparable or better performance over the previous work. For the 10 irregular matrices, the CSR5 obtains average performance improvement of 17.6\%, 28.5\%, 173.0\% and 293.3\% (up to 213.3\%, 153.6\%, 405.1\% and 943.3\%) over the best existing work on dual-socket Intel CPUs, an nVidia GPU, an AMD GPU and an Intel Xeon Phi, respectively. For real-world applications such as a solver with only tens of iterations, the CSR5 format can be more practical because of its low-overhead for format conversion. The source code of this work is downloadable at this https URL

Connected through hyperedge HE-1 (induced by source paper "a unified iteration space transformation framework for sparse and dense tensor algebra"):
  with members:
    - "load balanced sparse mttkrp on gpus"
    - "format abstraction for sparse tensor algebracompilers"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "tensorflow a system for large scale machine learning"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (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"
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"

Connected through hyperedge HE-3 (induced by source paper "automatic generation of efficient sparse tensor format conversion routines"):
  with members:
    - "format abstraction for sparse tensor algebracompilers"
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-4 (induced by source paper "smash co designing software compression and hardware accelerated indexing for efficient sparse matrix operations"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "sparse matrix multiplication on cam based accelerator"
    - "performance analysis and optimization of sparse matrix vector multiplication on modern multi and many core processors"
    - "graphr accelerating graph processing using reram"
    - "snap a general purpose network analysis and graph mining library"
    - "sparse 3d convolutional neural networks"
    - "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"

Connected through hyperedge HE-5 (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:
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"

Connected through hyperedge HE-6 (induced by source paper "performance analysis and optimization of sparse matrix vector multiplication on modern multi and many core processors"):
  with members:
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"

Connected through hyperedge HE-7 (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"
    - "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)