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: "generating families of practical fast matrix multiplication algorithms"
  Abstract: Matrix multiplication (GEMM) is a core operation to numerous scientific applications. Traditional implementations of Strassen-like fast matrix multiplication (FMM) algorithms often do not perform well except for very large matrix sizes, due to the increased cost of memory movement, which is particularly noticeable for non-square matrices. Such implementations also require considerable workspace and modifications to the standard BLAS interface. We propose a code generator framework to automatically implement a large family of FMM algorithms suitable for multiplications of arbitrary matrix sizes and shapes. By representing FMM with a triple of matrices [U,V,W] that capture the linear combinations of submatrices that are formed, we can use the Kronecker product to define a multi-level representation of Strassen-like algorithms. Incorporating the matrix additions that must be performed for Strassen-like algorithms into the inherent packing and micro-kernel operations inside GEMM avoids extra workspace and reduces the cost of memory movement. Adopting the same loop structures as high-performance GEMM implementations allows parallelization of all FMM algorithms with simple but efficient data parallelism without the overhead of task parallelism. We present a simple performance model for general FMM algorithms and compare actual performance of 20+ FMM algorithms to modeled predictions. Our implementations demonstrate a performance benefit over conventional GEMM on single core and multi-core systems. This study shows that Strassen-like fast matrix multiplication can be incorporated into libraries for practical use.

Connected through hyperedge HE-1 (induced by source paper "strassen s 2x2 matrix multiplication algorithm a conceptual perspective"):
  with members:
    - "the geometry of rank decompositions of matrix multiplication ii 3 times 3 matrices"
    - "designing strassen s algorithm"
    - "matrix multiplication algorithms from group orbits"
    - "fast matrix multiplication and symbolic computation"
    - "powers of tensors and fast matrix multiplication"
    - "on symmetries of the strassen algorithm"
    - "better late than never filling a void in the history of fast matrix multiplication and tensor decompositions"

Connected through hyperedge HE-2 (induced by source paper "deterministic combinatorial replacement paths and distance sensitivity oracles"):
  with members:
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "dynamic matching reducing integral algorithms to approximately maximal fractional algorithms"
    - "improved algorithms for decremental single source reachability on directed graphs"
    - "a framework for practical parallel fast matrix multiplication"
    - "decremental single source shortest paths on undirected graphs in near linear total update time"
    - "sublinear time decremental algorithms for single source reachability and shortest paths on directed graphs"
    - "replacement paths via row minima of concise matrices"

Connected through hyperedge HE-3 (induced by source paper "engineering boolean matrix multiplication for multiple accelerator shared memory architectures"):
  with members:
    - "fast feasible and unfeasible matrix multiplication"
    - "dissecting gpu memory hierarchy through microbenchmarking"
    - "an improved combinatorial algorithm for boolean matrix multiplication"
    - "a framework for practical parallel fast matrix multiplication"
    - "powers of tensors and fast matrix multiplication"
    - "communication optimal parallel algorithm for strassen s matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "supporting mixed datatype matrix multiplication within the blis framework"):
  with members:
    - "high performance tensor contraction without transposition"

Connected through hyperedge HE-5 (induced by source paper "a new algorithm for finding closest pair of vectors"):
  with members:
    - "an illuminating algorithm for the light bulb problem"
    - "fast feasible and unfeasible matrix multiplication"
    - "distributed pcp theorems for hardness of approximation in p"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "a framework for practical parallel fast matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "implementing strassen s algorithm with cutlass on nvidia volta gpus"):
  with members:
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "high performance tensor contraction without transposition"
    - "improving the numerical stability of fast matrix multiplication"
    - "a framework for practical parallel fast matrix multiplication"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "an adaptive prefix assignment technique for symmetry reduction"):
  with members:
    - "an improved isomorphism test for bounded tree width graphs"
    - "graph isomorphism in quasipolynomial time"
    - "breaking symmetries in graph search with canonizing sets"
    - "a framework for practical parallel fast matrix multiplication"
    - "fixed parameter tractable canonization and isomorphism test for graphs of bounded treewidth"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-8 (induced by source paper "strassen s algorithm for tensor contraction"):
  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"
    - "improving the numerical stability of fast matrix multiplication"
    - "a framework for practical parallel fast 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.MS(Mathematical Software)