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 "low overhead instruction latency characterization for nvidia gpgpus"):
  Abstract: The last decade has seen a shift in the computer systems industry where heterogeneous computing has become prevalent. Graphics Processing Units (GPUs) are now present in supercomputers to mobile phones and tablets. GPUs are used for graphics operations as well as general-purpose computing (GPGPUs) to boost the performance of compute-intensive applications. However, the percentage of undisclosed characteristics beyond what vendors provide is not small. In this paper, we introduce a very low overhead and portable analysis for exposing the latency of each instruction executing in the GPU pipeline(s) and the access overhead of the various memory hierarchies found in GPUs at the micro-architecture level. Furthermore, we show the impact of the various optimizations the CUDA compiler can perform over the various latencies. We perform our evaluation on seven different high-end NVIDIA GPUs from five different generations/architectures: Kepler, Maxwell, Pascal, Volta, and Turing. The results in this paper can help architects to have an accurate characterization of the latencies of these GPUs, which will help in modeling the hardware accurately. Also, software developers can perform informed optimizations to their applications.

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 "mixed precision training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "dissecting gpu memory hierarchy through microbenchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a study of the fundamental performance characteristics of gpus and cpus for database analytics extended version")

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)