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: "wide activation for efficient and accurate image super resolution"
  Abstract: In this report we demonstrate that with same parameters and computational budgets, models with wider features before ReLU activation have significantly better performance for single image super-resolution (SISR). The resulted SR residual network has a slim identity mapping pathway with wider (\(2\times\) to \(4\times\)) channels before activation in each residual block. To further widen activation (\(6\times\) to \(9\times\)) without computational overhead, we introduce linear low-rank convolution into SR networks and achieve even better accuracy-efficiency tradeoffs. In addition, compared with batch normalization or no normalization, we find training with weight normalization leads to better accuracy for deep super-resolution networks. Our proposed SR network \textit{WDSR} achieves better results on large-scale DIV2K image super-resolution benchmark in terms of PSNR with same or lower computational complexity. Based on WDSR, our method also won 1st places in NTIRE 2018 Challenge on Single Image Super-Resolution in all three realistic tracks. Experiments and ablation studies support the importance of wide activation for image super-resolution. Code is released at: this https URL

Connected through hyperedge HE-1 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way"):
  with members:
    - "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "multi style generative network for real time transfer"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring the regularity of sparse structure in convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "spatial spectral residual network for hyperspectral image super resolution"):
  with members:
    - "locality and structure regularized low rank representation for hyperspectral image classification"
    - "getnet a general end to end 2 d cnn framework for hyperspectral image change detection"
    - "unsupervised sparse dirichlet net for hyperspectral image super resolution"
    - "channel wise and spatial feature modulation network for single image super resolution"
    - "residual dense network for image super resolution"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "autoslim towards one shot architecture search for channel numbers"):
  with members:
    - "universally slimmable networks and improved training techniques"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "learning to prune filters in convolutional neural networks"
    - "slimmable neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "graph hypernetworks for neural architecture search"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-4 (induced by source paper "lightweight image super resolution with adaptive weighted learning network"):
  with members:
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "deep learning for image super resolution a survey"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "residual convolutional neural network revisited with active weighted mapping"
    - "image super resolution using very deep residual channel attention networks"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"

Connected through hyperedge HE-5 (induced by source paper "universally slimmable networks and improved training techniques"):
  with members:
    - "autoslim towards one shot architecture search for channel numbers"
    - "slimmable neural networks"
    - "efficient video object segmentation via network modulation"
    - "free form image inpainting with gated convolution"
    - "label refinery improving imagenet classification through label progression"
    - "generative image inpainting with contextual attention"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "scale wise convolution for image restoration"):
  with members:
    - "non local recurrent network for image restoration"
    - "sniper efficient multi scale training"
    - "image super resolution via dual state recurrent networks"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "dssd deconvolutional single shot detector"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-7 (induced by source paper "blind super resolution kernel estimation using an internal gan"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "blind super resolution with iterative kernel correction"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "image super resolution using very deep residual channel attention networks"
    - "ingan capturing and remapping the dna of a natural image"
    - "spectral normalization for generative adversarial networks"
    - "a fully progressive approach to single image super resolution"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-8 (induced by source paper "edge aware deep image deblurring"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "a neural approach to blind motion deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep video deblurring"
    - "from motion blur to motion flow a deep learning solution for removing heterogeneous motion blur"

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.CV(Computer Vision and Pattern Recognition)