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: "on demand learning for deep image restoration"
  Abstract: While machine learning approaches to image restoration offer great promise, current methods risk training models fixated on performing well only for image corruption of a particular level of difficulty---such as a certain level of noise or blur. First, we examine the weakness of conventional "fixated" models and demonstrate that training general models to handle arbitrary levels of corruption is indeed non-trivial. Then, we propose an on-demand learning algorithm for training image restoration models with deep convolutional neural networks. The main idea is to exploit a feedback mechanism to self-generate training instances where they are needed most, thereby learning models that can generalize across difficulty levels. On four restoration tasks---image inpainting, pixel interpolation, image deblurring, and image denoising---and three diverse datasets, our approach consistently outperforms both the status quo training procedure and curriculum learning alternatives.

Connected through hyperedge HE-1 (induced by source paper "restore from restored video restoration with pseudo clean video"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "residual non local attention networks for image restoration"
    - "noise2self blind denoising by self supervision"
    - "real image denoising with feature attention"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "frame recurrent video super resolution"
    - "residual dense network for image restoration"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"

Connected through hyperedge HE-2 (induced by source paper "blind image restoration without prior knowledge"):
  with members:
    - "blind universal bayesian image denoising with gaussian noise level learning"
    - "blind super resolution with iterative kernel correction"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "deep network interpolation for continuous imagery effect transition"

Connected through hyperedge HE-3 (induced by source paper "deep likelihood network for image restoration with multiple degradations"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "the perception distortion tradeoff"
    - "on the learning dynamics of deep neural networks"
    - "physics based generative adversarial models for image restoration and beyond"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "deep image prior"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-4 (induced by source paper "feedback network for image super resolution"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "top down feedback for crowd counting convolutional neural network"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "image super resolution via dual state recurrent networks"

Connected through hyperedge HE-5 (induced by source paper "adaptive image sampling using deep learning and its application on x ray fluorescence image reconstruction"):
  with members:
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "multi modal image processing based on coupled dictionary learning"
    - "deep fully connected networks for video compressive sensing"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "semantic image inpainting with deep generative models"
    - "deepbinarymask learning a binary mask for video compressive sensing"
    - "context encoders feature learning by inpainting"
    - "semantic image inpainting with perceptual and contextual losses"

Connected through hyperedge HE-6 (induced by source paper "training and refining deep learning based denoisers without ground truth data"):
  with members:
    - "noise2noise learning image restoration without clean data"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "training deep learning based denoisers without ground truth data"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "contemplating visual emotions understanding and overcoming dataset bias"):
  with members:
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "webvision database visual learning and understanding from web data"
    - "collaborative summarization of topic related videos"
    - "automatic understanding of image and video advertisements"
    - "multi task curriculum transfer deep learning of clothing attributes"

Connected through hyperedge HE-8 (induced by source paper "x gans image reconstruction made easy for extreme cases"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "learning representations for automatic colorization"
    - "generative multi adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "semantic image inpainting with deep generative models"
    - "context encoders feature learning by inpainting"

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)