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: "towards an efficient use of the blas library for multilinear tensor contractions"
  Abstract: Mathematical operators whose transformation rules constitute the building blocks of a multi-linear algebra are widely used in physics and engineering applications where they are very often represented as tensors. In the last century, thanks to the advances in tensor calculus, it was possible to uncover new research fields and make remarkable progress in the existing ones, from electromagnetism to the dynamics of fluids and from the mechanics of rigid bodies to quantum mechanics of many atoms. By now, the formal mathematical and geometrical properties of tensors are well defined and understood; conversely, in the context of scientific and high-performance computing, many tensor- related problems are still open. In this paper, we address the problem of efficiently computing contractions among two tensors of arbitrary dimension by using kernels from the highly optimized BLAS library. In particular, we establish precise conditions to determine if and when GEMM, the kernel for matrix products, can be used. Such conditions take into consideration both the nature of the operation and the storage scheme of the tensors, and induce a classification of the contractions into three groups. For each group, we provide a recipe to guide the users towards the most effective use of BLAS.

Connected through hyperedge HE-1 (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"
    - "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"
    - "performance modeling for dense linear algebra"

Connected through hyperedge HE-2 (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"
    - "high performance tensor contraction without transposition"
    - "ttc a tensor transposition compiler for multiple architectures"

Connected through hyperedge HE-3 (induced by source paper "tlib a flexible c tensor framework for numerical tensor calculus"):
  with members:
    - "runtime flexible multi dimensional arrays and views for c 98 and c 0x"

Connected through hyperedge HE-4 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  with members:
    - "randomized single view algorithms for low rank matrix approximation"
    - "group component analysis for multiblock data common and individual feature extraction"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "parallel tensor compression for large scale scientific data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"

Connected through hyperedge HE-5 (induced by source paper "design of a high performance gemm like tensor tensor multiplication"):
  with members:
    - "hptt a high performance tensor transposition c library"
    - "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"
    - "tensor contractions with extended blas kernels on cpu and gpu"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-6 (induced by source paper "ttc a high performance compiler for tensor transpositions"):
  with members:
    - "hptt a high performance tensor transposition c library"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "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"
    - "cudnn efficient primitives for deep learning"
    - "p3dfft a framework for parallel computations of fourier transforms in three dimensions"

Connected through hyperedge HE-7 (induced by source paper "high performance tensor contraction without transposition"):
  with members:
    - "ttc a high performance compiler for tensor transpositions"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "task based algorithm for matrix multiplication a step towards block sparse tensor computing"
    - "scalable task based algorithm for multiplication of block rank sparse matrices"
    - "on the performance prediction of blas based tensor contractions"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-8 (induced by source paper "tensor contractions with extended blas kernels on cpu and gpu"):
  with members:
    - "on the performance prediction of blas based tensor contractions"
    - "a gemm interface and implementation on nvidia gpus for multiple small matrices"
    - "fourier pca and robust tensor decomposition"
    - "performance modeling for dense linear algebra"
    - "tensor decompositions for learning latent variable models"

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)