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 fully convolutional networks for iterative non blind deconvolution"
  Abstract: In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image deconvolution. We train a FCNN to remove noises in the gradient domain and use the learned gradients to guide the image deconvolution step. In contrast to the existing deep neural network based methods, we iteratively deconvolve the blurred images in a multi-stage framework. The proposed method is able to learn an adaptive image prior, which keeps both local (details) and global (structures) information. Both quantitative and qualitative evaluations on benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of quality and speed.

Connected through hyperedge HE-1 (induced by source paper "deep blind video super resolution"):
  with members:
    - "blind super resolution with iterative kernel correction"
    - "blind super resolution kernel estimation using an internal gan"
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "recurrent back projection network for video super resolution"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"
    - "neural blind deconvolution using deep priors"
    - "frame recurrent video super resolution"

Connected through hyperedge HE-2 (induced by source paper "learning deep gradient descent optimization for image deconvolution"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "learning aggregated transmission propagation networks for haze removal and beyond"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-3 (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-4 (induced by source paper "davanet stereo deblurring with view aggregation"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "stereoscopic neural style transfer"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "pyramid stereo matching network"
    - "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-5 (induced by source paper "integrating neural networks into the blind deblurring framework to compete with the end to end learning based methods"):
  with members:
    - "denoising prior driven deep neural network for image restoration"
    - "learning a discriminative prior for blind image deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "deep image matting"
    - "learning deep cnn denoiser prior for image restoration"
    - "detail revealing deep video super resolution"
    - "image restoration using autoencoding priors"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-6 (induced by source paper "learning deep neural networks in blind deblurring framework"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "learning a discriminative prior for blind image deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "deep image matting"
    - "learning deep cnn denoiser prior for image restoration"
    - "detail revealing deep video super resolution"
    - "image restoration using autoencoding priors"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "optimal transport cyclegan and penalized ls for unsupervised learning in inverse problems"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
    - "deep compressed sensing"
    - "k space deep learning for accelerated mri"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "bi skip a motion deblurring network using self paced learning"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "deep image prior"
    - "learning blind motion deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "improved training of wasserstein gans"
    - "perceptual losses for real time style transfer and super resolution"
    - "improved techniques for training gans"
    - "deep multi scale convolutional neural network for dynamic scene 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)