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: "machine learning based auto tuning for enhanced opencl performance portability"
  Abstract: Heterogeneous computing, which combines devices with different architectures, is rising in popularity, and promises increased performance combined with reduced energy consumption. OpenCL has been proposed as a standard for programing such systems, and offers functional portability. It does, however, suffer from poor performance portability, code tuned for one device must be re-tuned to achieve good performance on another device. In this paper, we use machine learning-based auto-tuning to address this problem. Benchmarks are run on a random subset of the entire tuning parameter configuration space, and the results are used to build an artificial neural network based model. The model can then be used to find interesting parts of the parameter space for further search. We evaluate our method with different benchmarks, on several devices, including an Intel i7 3770 CPU, an Nvidia K40 GPU and an AMD Radeon HD 7970 GPU. Our model achieves a mean relative error as low as 6.1%, and is able to find configurations as little as 1.3% worse than the global minimum.

Connected through hyperedge HE-1 (induced by source paper "towards automated kernel selection in machine learning systems a sycl case study"):
  with members:
    - "accelerated neural networks on opencl devices using sycl dnn"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "clblast a tuned opencl blas library"
    - "deep residual learning for image recognition"
    - "cltune a generic auto tuner for opencl kernels"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "a survey on compiler autotuning using machine learning"):
  with members:
    - "less is more exploiting the standard compiler optimization levels for better performance and energy consumption"
    - "compiler phase ordering as an orthogonal approach for reducing energy consumption"
    - "a collective knowledge workflow for collaborative research into multi objective autotuning and machine learning techniques"
    - "automatically tuning the gcc compiler to optimize the performance of applications running on embedded systems"
    - "collective mind part ii towards performance and cost aware software engineering as a natural science"
    - "autotuning opencl workgroup size for stencil patterns"
    - "iterative compilation on mobile devices"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "clblast a tuned opencl blas library"):
  with members:
    - "input aware auto tuning of compute bound hpc kernels"
    - "sobol tensor trains for global sensitivity analysis"
    - "mixed precision training"
    - "parallel multi channel convolution using general matrix multiplication"
    - "cltune a generic auto tuner for opencl kernels"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-4 (induced by source paper "a model driven approach for a new generation of adaptive libraries"):
  with members:
    - "input aware auto tuning of compute bound hpc kernels"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "cltune a generic auto tuner for opencl kernels"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "imagecl an image processing language for performance portability on heterogeneous systems"):
  with members:
    - "cltune a generic auto tuner for opencl kernels"

Connected through hyperedge HE-6 (induced by source paper "autotuning opencl workgroup size for stencil patterns"):
  with members:
    - "cltune a generic auto tuner for opencl kernels"

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)