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: "hptt a high performance tensor transposition c library"
  Abstract: Recently we presented TTC, a domain-specific compiler for tensor transpositions. Despite the fact that the performance of the generated code is nearly optimal, due to its offline nature, TTC cannot be utilized in all the application codes in which the tensor sizes and the necessary tensor permutations are determined at runtime. To overcome this limitation, we introduce the open-source C++ library High-Performance Tensor Transposition (HPTT). Similar to TTC, HPTT incorporates optimizations such as blocking, multi-threading, and explicit vectorization; furthermore it decomposes any transposition into multiple loops around a so called micro-kernel. This modular design---inspired by BLIS---makes HPTT easy to port to different architectures, by only replacing the hand-vectorized micro-kernel (e.g., a 4x4 transpose). HPTT also offers an optional autotuning framework---guided by a performance model---that explores a vast search space of implementations at runtime (similar to FFTW). Across a wide range of different tensor transpositions and architectures (e.g., Intel Ivy Bridge, Intel Knights Landing, ARMv7, IBM Power7), HPTT attains a bandwidth comparable to that of SAXPY, and yields remarkable speedups over Eigen's tensor transposition implementation. Most importantly, the integration of HPTT into the Cyclops Tensor Framework (CTF) improves the overall performance of tensor contractions by up to 3.1x.

Connected through hyperedge HE-1 (induced by source paper "enabling distributed memory tensor completion in python using new sparse tensor kernels"):
  with members:
    - "pasta a parallel sparse tensor algorithm benchmark suite"
    - "format abstraction for sparse tensor algebracompilers"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "shared memory parallelization of mttkrp for dense tensors"
    - "communication lower bounds for matricized tensor times khatri rao product"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "tensorflow a system for large scale machine learning"
    - "sparse tensor algebra as a parallel programming model"

Connected through hyperedge HE-2 (induced by source paper "spin summations a high performance perspective"):
  with members:
    - "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"
    - "towards an efficient use of the blas library for multilinear tensor contractions"

Connected through hyperedge HE-3 (induced by source paper "design of a high performance gemm like tensor tensor multiplication"):
  with members:
    - "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"
    - "towards an efficient use of the blas library for multilinear tensor contractions"

Connected through hyperedge HE-4 (induced by source paper "ttc a high performance compiler for tensor transpositions"):
  with members:
    - "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"
    - "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)