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: "cltune a generic auto tuner for opencl kernels"
  Abstract: This work presents CLTune, an auto-tuner for OpenCL kernels. It evaluates and tunes kernel performance of a generic, user-defined search space of possible parameter-value combinations. Example parameters include the OpenCL workgroup size, vector data-types, tile sizes, and loop unrolling factors. CLTune can be used in the following scenarios: 1) when there are too many tunable parameters to explore manually, 2) when performance portability across OpenCL devices is desired, or 3) when the optimal parameters change based on input argument values (e.g. matrix dimensions). The auto-tuner is generic, easy to use, open-source, and supports multiple search strategies including simulated annealing and particle swarm optimisation. CLTune is evaluated on two GPU case-studies inspired by the recent successes in deep learning: 2D convolution and matrix-multiplication (GEMM). For 2D convolution, we demonstrate the need for auto-tuning by optimizing for different filter sizes, achieving performance on-par or better than the state-of-the-art. For matrix-multiplication, we use CLTune to explore a parameter space of more than two-hundred thousand configurations, we show the need for device-specific tuning, and outperform the clBLAS library on NVIDIA, AMD and Intel GPUs.

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"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "the nn stacking feature weighted linear stacking through neural networks"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "feature weighted linear stacking"

Connected through hyperedge HE-3 (induced by source paper "a benchmark set of highly efficient cuda and opencl kernels and its dynamic autotuning with kernel tuning toolkit"):
  with members:
    - "optimizing cuda code by kernel fusion application on blas"

Connected through hyperedge HE-4 (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"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-5 (induced by source paper "dropout based active learning for regression"):
  with members:
    - "deep bayesian active learning with image data"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "theano a python framework for fast computation of mathematical expressions"
    - "adam a method for stochastic optimization"
    - "a bayesian encourages dropout"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-6 (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"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "sobol tensor trains for global sensitivity analysis"):
  with members:
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition"

Connected through hyperedge HE-8 (induced by source paper "cltorch a hardware agnostic backend for the torch deep neural network library based on opencl"):
  with members:
    - "gemmbench a framework for reproducible and collaborative benchmarking of matrix multiplication"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "a performance comparison of sort and scan libraries for gpus"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "fast algorithms for convolutional neural networks"
    - "going deeper with convolutions"

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.PF(Performance)