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: "design of a high performance gemm like tensor tensor multiplication"
  Abstract: We present "GEMM-like Tensor-Tensor multiplication" (GETT), a novel approach to tensor contractions that mirrors the design of a high-performance general matrix-matrix multiplication (GEMM). The critical insight behind GETT is the identification of three index sets, involved in the tensor contraction, which enable us to systematically reduce an arbitrary tensor contraction to loops around a highly tuned "macro-kernel". This macro-kernel operates on suitably prepared ("packed") sub-tensors that reside in a specified level of the cache hierarchy. In contrast to previous approaches to tensor contractions, GETT exhibits desirable features such as unit-stride memory accesses, cache-awareness, as well as full vectorization, without requiring auxiliary memory. To compare our technique with other modern tensor contractions, we integrate GETT alongside the so called Transpose-Transpose-GEMM-Transpose and Loops-over-GEMM approaches into an open source "Tensor Contraction Code Generator" (TCCG). The performance results for a wide range of tensor contractions suggest that GETT has the potential of becoming the method of choice: While GETT exhibits excellent performance across the board, its effectiveness for bandwidth-bound tensor contractions is especially impressive, outperforming existing approaches by up to $12.4\times$. More precisely, GETT achieves speedups of up to $1.41\times$ over an equivalent-sized GEMM for bandwidth-bound tensor contractions while attaining up to $91.3\%$ of peak floating-point performance for compute-bound tensor contractions.

Connected through hyperedge HE-1 (induced by source paper "yet another tensor toolbox for discontinuous galerkin methods and other applications"):
  with members:
    - "high performance tensor contraction without transposition"
    - "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-2 (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"
    - "high performance tensor contraction without transposition"
    - "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-3 (induced by source paper "format abstraction for sparse tensor algebracompilers"):
  with members:
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "high performance tensor contraction without transposition"
    - "tensorflow a system for large scale machine learning"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-4 (induced by source paper "modeling of languages for tensor manipulation"):
  with members:
    - "typesafe abstractions for tensor operations short paper"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "hptt a high performance tensor transposition c library"):
  with members:
    - "cutt a high performance tensor transpose library for cuda compatible gpus"
    - "ttc a high performance compiler for tensor transpositions"
    - "madness a multiresolution adaptive numerical environment for scientific simulation"
    - "ttc a tensor transposition compiler for multiple architectures"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "p3dfft a framework for parallel computations of fourier transforms in three dimensions"

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"
    - "high performance tensor contraction without transposition"
    - "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"
    - "high performance tensor contraction without transposition"
    - "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 "tsfc a structure preserving form compiler"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "cross loop optimization of arithmetic intensity for finite element local assembly"
    - "automated code generation for discontinuous galerkin methods"

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)