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: "benchmarking opencl openacc openmp and cuda programming productivity performance and energy consumption"
  Abstract: Many modern parallel computing systems are heterogeneous at their node level. Such nodes may comprise general purpose CPUs and accelerators (such as, GPU, or Intel Xeon Phi) that provide high performance with suitable energy-consumption characteristics. However, exploiting the available performance of heterogeneous architectures may be challenging. There are various parallel programming frameworks (such as, OpenMP, OpenCL, OpenACC, CUDA) and selecting the one that is suitable for a target context is not straightforward. #R##N#In this paper, we study empirically the characteristics of OpenMP, OpenACC, OpenCL, and CUDA with respect to programming productivity, performance, and energy. To evaluate the programming productivity we use our homegrown tool CodeStat, which enables us to determine the percentage of code lines that was required to parallelize the code using a specific framework. We use our tool x-MeterPU to evaluate the energy consumption and the performance. Experiments are conducted using the industry-standard SPEC benchmark suite and the Rodinia benchmark suite for accelerated computing on heterogeneous systems that combine Intel Xeon E5 Processors with a GPU accelerator or an Intel Xeon Phi co-processor.

Connected through hyperedge HE-1 (induced by source paper "performance modelling of deep learning on intel many integrated core architectures"):
  with members:
    - "using meta heuristics and machine learning for software optimization of parallel computing systems a systematic literature review"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "deep learning in neural networks"
    - "sequence to sequence learning with neural networks"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "gpu computing with python performance energy efficiency and usability"):
  with members:
    - "a comparative study of programming languages in rosetta code"
    - "best practices for scientific computing"
    - "pycuda and pyopencl a scripting based approach to gpu run time code generation"
    - "a performance comparison of cuda and opencl"

Connected through hyperedge HE-3 (induced by source paper "using meta heuristics and machine learning for software optimization of parallel computing systems a systematic literature review"):
  with members:
    - "combinatorial optimization of work distribution on heterogeneous systems"
    - "a self adaptive auto scaling method for scientific applications on hpc environments and clouds"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-4 (induced by source paper "backus comprehensive high performance research software engineering approach for simulations in supercomputing systems"):
  with members:
    - "the eclipse integrated computational environment"

Connected through hyperedge HE-5 (induced by source paper "hstream a directive based language extension for heterogeneous stream computing"):
  with members:
    - "using meta heuristics and machine learning for software optimization of parallel computing systems a systematic literature review"
    - "analyzing large scale dna sequences on multi core architectures"

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.DC(Distributed, Parallel, and Cluster Computing)