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: "high performance tensor contraction without transposition"
  Abstract: Tensor computations--in particular tensor contraction (TC)--are important kernels in many scientific computing applications. Due to the fundamental similarity of TC to matrix multiplication (MM) and to the availability of optimized implementations such as the BLAS, tensor operations have traditionally been implemented in terms of BLAS operations, incurring both a performance and a storage overhead. Instead, we implement TC using the flexible BLIS framework, which allows for transposition (reshaping) of the tensor to be fused with internal partitioning and packing operations, requiring no explicit transposition operations or additional workspace. This implementation, TBLIS, achieves performance approaching that of MM, and in some cases considerably higher than that of traditional TC. Our implementation supports multithreading using an approach identical to that used for MM in BLIS, with similar performance characteristics. The complexity of managing tensor-to-matrix transformations is also handled automatically in our approach, greatly simplifying its use in scientific applications.

Connected through hyperedge HE-1 (induced by source paper "a parallel sparse tensor benchmark suite on cpus and gpus"):
  with members:
    - "pasta a parallel sparse tensor algorithm benchmark suite"
    - "format abstraction for sparse tensor algebracompilers"
    - "spartan scalable parafac2 for large sparse data"
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "simtensor a synthetic tensor data generator"
    - "tensorizing neural networks"
    - "the gap benchmark suite"
    - "speeding up convolutional neural networks using fine tuned cp decomposition"

Connected through hyperedge HE-2 (induced by source paper "yet another tensor toolbox for discontinuous galerkin methods and other applications"):
  with members:
    - "design of a high performance gemm like tensor tensor multiplication"
    - "madness a multiresolution adaptive numerical environment for scientific simulation"
    - "tensor contractions with extended blas kernels on cpu and gpu"
    - "cross loop optimization of arithmetic intensity for finite element local assembly"
    - "towards an efficient use of the blas library for multilinear tensor contractions"
    - "performance modeling for dense linear algebra"

Connected through hyperedge HE-3 (induced by source paper "supporting mixed datatype matrix multiplication within the blis framework"):
  with members:
    - "generating families of practical fast matrix multiplication algorithms"

Connected through hyperedge HE-4 (induced by source paper "implementing strassen s algorithm with cutlass on nvidia volta gpus"):
  with members:
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "generating families of practical fast matrix multiplication algorithms"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "improving the numerical stability of fast matrix multiplication"
    - "a framework for practical parallel fast matrix multiplication"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "format abstraction for sparse tensor algebracompilers"):
  with members:
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "tensorflow a system for large scale machine learning"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-6 (induced by source paper "strassen s algorithm for tensor contraction"):
  with members:
    - "generating families of practical fast matrix multiplication algorithms"
    - "ttc a high performance compiler for tensor transpositions"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "improving the numerical stability of fast matrix multiplication"
    - "a framework for practical parallel fast matrix multiplication"

Connected through hyperedge HE-7 (induced by source paper "spin summations a high performance perspective"):
  with members:
    - "hptt a high performance tensor transposition c library"
    - "ttc a high performance compiler for tensor transpositions"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "ttc a tensor transposition compiler for multiple architectures"
    - "towards an efficient use of the blas library for multilinear tensor contractions"

Connected through hyperedge HE-8 (induced by source paper "cutt a high performance tensor transpose library for cuda compatible gpus"):
  with members:
    - "design of a high performance gemm like tensor tensor multiplication"
    - "parallel tensor compression for large scale scientific data"
    - "ttc a tensor transposition compiler for multiple architectures"

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)