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: "improving the numerical stability of fast matrix multiplication"
  Abstract: Fast algorithms for matrix multiplication, namely those that perform asymptotically fewer scalar operations than the classical algorithm, have been considered primarily of theoretical interest. Apart from Strassen's original algorithm, few fast algorithms have been efficiently implemented or used in practical applications. However, there exist many practical alternatives to Strassen's algorithm with varying performance and numerical properties. Fast algorithms are known to be numerically stable, but because their error bounds are slightly weaker than the classical algorithm, they are not used even in cases where they provide a performance benefit. #R##N#We argue in this paper that the numerical sacrifice of fast algorithms, particularly for the typical use cases of practical algorithms, is not prohibitive, and we explore ways to improve the accuracy both theoretically and empirically. The numerical accuracy of fast matrix multiplication depends on properties of the algorithm and of the input matrices, and we consider both contributions independently. We generalize and tighten previous error analyses of fast algorithms and compare their properties. We discuss algorithmic techniques for improving the error guarantees from two perspectives: manipulating the algorithms, and reducing input anomalies by various forms of diagonal scaling. Finally, we benchmark performance and demonstrate our improved numerical accuracy.

Connected through hyperedge HE-1 (induced by source paper "randomization of approximate bilinear computation for matrix multiplication"):
  with members:
    - "training deep neural networks with 8 bit floating point numbers"
    - "implementing strassen s algorithm with cutlass on nvidia volta gpus"
    - "deep learning with limited numerical precision"
    - "a framework for practical parallel fast matrix multiplication"
    - "compressed matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "equivalent polyadic decompositions of matrix multiplication tensors"):
  with members:
    - "a framework for practical parallel fast matrix multiplication"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "implementing strassen s algorithm with cutlass on nvidia volta gpus"):
  with members:
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "generating families of practical fast matrix multiplication algorithms"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "high performance tensor contraction without transposition"
    - "a framework for practical parallel fast matrix multiplication"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "strassen s algorithm for tensor contraction"):
  with members:
    - "generating families of practical fast matrix multiplication algorithms"
    - "ttc a high performance compiler for tensor transpositions"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "high performance tensor contraction without transposition"
    - "a framework for practical parallel fast matrix multiplication"

Connected through hyperedge HE-5 (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-6 (induced by source paper "fast matrix multiplication and symbolic computation"):
  with members:
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "a framework for practical parallel fast matrix multiplication"
    - "fast matrix multiplication limitations of the laser method"
    - "elements of design for containers and solutions in the linbox library"
    - "bitslicing and the method of four russians over larger finite fields"

Connected through hyperedge HE-7 (induced by source paper "generating families of practical fast matrix multiplication algorithms"):
  with members:
    - "the i o complexity of strassen s matrix multiplication with recomputation"
    - "a framework for practical parallel fast matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"

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.NA(Numerical Analysis)