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 "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"):
  Abstract: Understanding the scalability of parallel programs is crucial for software optimization and hardware architecture design. As HPC hardware is moving towards many-core design, it becomes increasingly difficult for a parallel program to make effective use of all available processor cores. This makes scalability analysis increasingly important. This paper presents a quantitative study for characterizing the scalability of sparse matrix–vector multiplications (SpMV) on Phytium FT-2000+, an ARM-based HPC many-core architecture. We choose SpMV as it is a common operation in scientific and HPC applications. Due to the newness of ARM-based many-core architectures, there is little work on understanding the SpMV scalability on such hardware design. To close the gap, we carry out a large-scale empirical evaluation involved over 1000 representative SpMV datasets. We show that, while many computation-intensive SpMV applications contain extensive parallelism, achieving a linear speedup is non-trivial on Phytium FT-2000+. To better understand what software and hardware parameters are most important for determining the scalability of a given SpMV kernel, we develop a performance analytical model based on the regression tree. We show that our model is highly effective in characterizing SpMV scalability, offering useful insights to help application developers for better optimizing SpMV on an emerging HPC architecture.

Includes members:
  Member "optimizing deep learning inference on embedded systems through adaptive model selection":
    also connected through other hyperedges:
      (none)
  Member "machine learning in compiler optimisation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "improving spark application throughput via memory aware task co location a mixture of experts approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing streaming parallelism on heterogeneous many core architectures")
  Member "speculative segmented sum for sparse matrix vector multiplication on heterogeneous processors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a recursive algebraic coloring technique for hardware efficient symmetric sparse matrix vector multiplication")
  Member "csr5 an efficient storage format for cross platform sparse matrix vector multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a unified iteration space transformation framework for sparse and dense tensor algebra")
  Member "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a recursive algebraic coloring technique for hardware efficient symmetric sparse matrix vector multiplication")

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.DC(Distributed, Parallel, and Cluster Computing)