human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "hptt a high performance tensor transposition c library":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enabling distributed memory tensor completion in python using new sparse tensor kernels")
  Member "ttc a high performance compiler for tensor transpositions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hptt a high performance tensor transposition c library")
  Member "madness a multiresolution adaptive numerical environment for scientific simulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "yet another tensor toolbox for discontinuous galerkin methods and other applications")
  Member "ttc a tensor transposition compiler for multiple architectures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hptt a high performance tensor transposition c library")
  Member "tensor contractions with extended blas kernels on cpu and gpu":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "high performance deep learning via a single building block")
  Member "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "fast convolutional nets with fbfft a gpu performance evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploiting verified neural networks via floating point numerical error")
  Member "cudnn efficient primitives for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "searching for winograd aware quantized networks")

Question: Please tell me which class the source paper 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)