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 "implementing strassen s algorithm with cutlass on nvidia volta gpus"):
  Abstract: Conventional GPU implementations of Strassen's algorithm (Strassen) typically rely on the existing high-performance matrix multiplication (GEMM), trading space for time. As a result, such approaches can only achieve practical speedup for relatively large, "squarish" matrices due to the extra memory overhead, and their usages are limited due to the considerable workspace. We present novel Strassen primitives for GPUs that can be composed to generate a family of Strassen algorithms. Our algorithms utilize both the memory and thread hierarchies on GPUs, reusing shared memory and register files inherited from GEMM, fusing additional operations, and avoiding extra workspace. We further exploit intra- and inter-kernel parallelism by batching, streaming, and employing atomic operations. We also develop a performance model for NVIDIA Volta GPUs to select the appropriate blocking parameters and predict the performance for GEMM and Strassen. Overall, our 1-level Strassen can achieve up to 1.11x speedup with a crossover point as small as 1,536 compared to cublasSgemm on a NVIDIA Tesla V100 GPU. With additional workspace, our 2-level Strassen can achieve 1.19x speedup with a crossover point at 7,680.

Includes members:
  Member "dissecting the nvidia volta gpu architecture via microbenchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gpu tensor cores for fast arithmetic reductions")
  Member "generating families of practical fast matrix multiplication algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strassen s 2x2 matrix multiplication algorithm a conceptual perspective")
  Member "design of a high performance gemm like tensor tensor multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "yet another tensor toolbox for discontinuous galerkin methods and other applications")
  Member "high performance tensor contraction without transposition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a parallel sparse tensor benchmark suite on cpus and gpus")
  Member "improving the numerical stability of fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "randomization of approximate bilinear computation for matrix multiplication")
  Member "a framework for practical parallel fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deterministic combinatorial replacement paths and distance sensitivity oracles")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)