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: "learning to deblur"
  Abstract: We describe a learning-based approach to blind image deconvolution. It uses a deep layered architecture, parts of which are borrowed from recent work on neural network learning, and parts of which incorporate computations that are specific to image deconvolution. The system is trained end-to-end on a set of artificially generated training examples, enabling competitive performance in blind deconvolution, both with respect to quality and runtime.

Connected through hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "noise robust generative adversarial networks"
    - "high quality self supervised deep image denoising"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "noise2self blind denoising by self supervision"
    - "diversity sensitive conditional generative adversarial networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"

Connected through hyperedge HE-2 (induced by source paper "replacing mobile camera isp with a single deep learning model"):
  with members:
    - "ai benchmark all about deep learning on smartphones in 2019"
    - "aim 2019 challenge on image demoireing dataset and study"
    - "semantic image synthesis with spatially adaptive normalization"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "ai benchmark running deep neural networks on android smartphones"
    - "fast perceptual image enhancement"

Connected through hyperedge HE-3 (induced by source paper "semantic aware image deblurring"):
  with members:
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"

Connected through hyperedge HE-4 (induced by source paper "david dual attentional video deblurring"):
  with members:
    - "segmentation aware image denoising without knowing true segmentation"
    - "learning to optimize in swarms"
    - "deblurgan v2 deblurring orders of magnitude faster and better"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "bridging the gap between computational photography and visual recognition"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"

Connected through hyperedge HE-5 (induced by source paper "a deep optimization approach for image deconvolution"):
  with members:
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "learning an optimizer for image deconvolution"
    - "scale recurrent network for deep image deblurring"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "learning a discriminative prior for blind image deblurring"
    - "blind image deconvolution using deep generative priors"
    - "learning collaborative generation correction modules for blind image deblurring and beyond"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-6 (induced by source paper "unsupervised domain specific deblurring via disentangled representations"):
  with members:
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "exploring disentangled feature representation beyond face identification"
    - "towards open set identity preserving face synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep semantic face deblurring"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-7 (induced by source paper "deep video deblurring the devil is in the details"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "down scaling with learned kernels in multi scale deep neural networks for non uniform single image deblurring"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "scale recurrent network for deep image deblurring"
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a neural approach to blind motion deblurring"

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)