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: "ttc a tensor transposition compiler for multiple architectures"
  Abstract: We consider the problem of transposing tensors of arbitrary dimension and describe TTC, an open source domain-specific parallel compiler. TTC generates optimized parallel C++/CUDA C code that achieves a significant fraction of the system's peak memory bandwidth. TTC exhibits high performance across multiple architectures, including modern AVX-based systems (e.g., Intel Haswell, AMD Steamroller), Intel's Knights Corner as well as different CUDA-based GPUs such as NVIDIA's Kepler and Maxwell architectures. We report speedups of TTC over a meaningful baseline implementation generated by external C++ compilers; the results suggest that a domain-specific compiler can outperform its general purpose counterpart significantly: For instance, comparing with Intel's latest C++ compiler on the Haswell and Knights Corner architecture, TTC yields speedups of up to 8x and 32x, respectively. We also showcase TTC's support for multiple leading dimensions, making it a suitable candidate for the generation of performance-critical packing functions that are at the core of the ubiquitous BLAS 3 routines.

Connected through hyperedge HE-1 (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"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "madness a multiresolution adaptive numerical environment for scientific simulation"
    - "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-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"
    - "towards an efficient use of the blas library for multilinear tensor contractions"

Connected through hyperedge HE-3 (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"
    - "high performance tensor contraction without transposition"
    - "parallel tensor compression for large scale scientific data"

Connected through hyperedge HE-4 (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"
    - "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"
    - "towards an efficient use of the blas library for multilinear tensor contractions"

Connected through hyperedge HE-5 (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"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "cudnn efficient primitives for deep learning"
    - "towards an efficient use of the blas library for multilinear tensor contractions"
    - "p3dfft a framework for parallel computations of fourier transforms in three dimensions"

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)