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: "on the performance prediction of blas based tensor contractions"
  Abstract: Tensor operations are surging as the computational building blocks for a variety of scientific simulations and the development of high-performance kernels for such operations is known to be a challenging task. While for operations on one- and two-dimensional tensors there exist standardized interfaces and highly-optimized libraries (BLAS), for higher dimensional tensors neither standards nor highly-tuned implementations exist yet. In this paper, we consider contractions between two tensors of arbitrary dimensionality and take on the challenge of generating high-performance implementations by resorting to sequences of BLAS kernels. The approach consists in breaking the contraction down into operations that only involve matrices or vectors. Since in general there are many alternative ways of decomposing a contraction, we are able to methodically derive a large family of algorithms. The main contribution of this paper is a systematic methodology to accurately identify the fastest algorithms in the bunch, without executing them. The goal is instead accomplished with the help of a set of cache-aware micro-benchmarks for the underlying BLAS kernels. The predictions we construct from such benchmarks allow us to reliably single out the best-performing algorithms in a tiny fraction of the time taken by the direct execution of the algorithms.

Connected through hyperedge HE-1 (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"
    - "towards an efficient use of the blas library for multilinear tensor contractions"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-2 (induced by source paper "tensor contractions with extended blas kernels on cpu and gpu"):
  with members:
    - "a gemm interface and implementation on nvidia gpus for multiple small matrices"
    - "fourier pca and robust tensor decomposition"
    - "towards an efficient use of the blas library for multilinear tensor contractions"
    - "performance modeling for dense linear algebra"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-3 (induced by source paper "the elaps framework experimental linear algebra performance studies"):
  with members:
    - "performance modeling for dense linear algebra"
    - "solving sequences of generalized least squares problems on multi threaded architectures"
    - "computing petaflops over terabytes of data the case of genome wide association studies"

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)