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 "gpu computing with python performance energy efficiency and usability"):
  Abstract: In this work, we examine the performance, energy efficiency and usability when using Python for developing HPC codes running on the GPU. We investigate the portability of performance and energy efficiency between CUDA and OpenCL; between GPU generations; and between low-end, mid-range and high-end GPUs. Our findings show that the impact of using Python is negligible for our applications, and furthermore, CUDA and OpenCL applications tuned to an equivalent level can in many cases obtain the same computational performance. Our experiments show that performance in general varies more between different GPUs than between using CUDA and OpenCL. We also show that tuning for performance is a good way of tuning for energy efficiency, but that specific tuning is needed to obtain optimal energy efficiency.

Includes members:
  Member "benchmarking opencl openacc openmp and cuda programming productivity performance and energy consumption":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance modelling of deep learning on intel many integrated core architectures")
  Member "a comparative study of programming languages in rosetta code":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the impact of programming languages on code quality")
  Member "best practices for scientific computing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a compositional framework for scientific model augmentation")
  Member "pycuda and pyopencl a scripting based approach to gpu run time code generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chainer a deep learning framework for accelerating the research cycle")
  Member "a performance comparison of cuda and opencl":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a unified optimization approach for cnn model inference on integrated gpus")

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)