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: "image restoration using autoencoding priors"
  Abstract: We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We build on the key observation that the output of an optimal denoising autoencoder is a local mean of the true data density, and the autoencoder error (the difference between the output and input of the trained autoencoder) is a mean shift vector. We use the magnitude of this mean shift vector, that is, the distance to the local mean, as the negative log likelihood of our natural image prior. For image restoration, we maximize the likelihood using gradient descent by backpropagating the autoencoder error. A key advantage of our approach is that we do not need to train separate networks for different image restoration tasks, such as non-blind deconvolution with different kernels, or super-resolution at different magnification factors. We demonstrate state of the art results for non-blind deconvolution and super-resolution using the same autoencoding prior.

Connected through hyperedge HE-1 (induced by source paper "learning generative models using denoising density estimators"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "a style based generator architecture for generative adversarial networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "implicit maximum likelihood estimation"
    - "deep mean shift priors for image restoration"
    - "progressive growing of gans for improved quality stability and variation"
    - "searching for activation functions"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (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-3 (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"
    - "video frame synthesis using deep voxel flow"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-4 (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"
    - "stacked hourglass networks for human pose estimation"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "domain translation with conditional gans from depth to rgb face to face"):
  with members:
    - "transforming sensor data to the image domain for deep learning an application to footstep detection"
    - "from depth data to head pose estimation a siamese approach"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep head pose estimation from depth data for in car automotive applications"
    - "face from depth for head pose estimation on depth images"
    - "generative adversarial models for people attribute recognition in surveillance"
    - "poseidon face from depth for driver pose estimation"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

Connected through hyperedge HE-6 (induced by source paper "algorithms and software for projections onto intersections of convex and non convex sets with applications to inverse problems"):
  with members:
    - "a unified 2d 3d large scale software environment for nonlinear inverse problems"
    - "adaptive relaxed admm convergence theory and practical implementation"
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "adaptive consensus admm for distributed optimization"
    - "devito automated fast finite difference computation"
    - "adaptive admm with spectral penalty parameter selection"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "jinv a flexible julia package for pde parameter estimation"

Connected through hyperedge HE-7 (induced by source paper "denoising prior driven deep neural network for image restoration"):
  with members:
    - "deep learning for generic object detection a survey"
    - "a survey of recent advances in texture representation"
    - "image super resolution via dual state recurrent networks"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "regularization by denoising clarifications and new interpretations"):
  with members:
    - "local kernels that approximate bayesian regularization and proximal operators"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "scene adapted plug and play algorithm with guaranteed convergence applications to data fusion in imaging"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "acceleration of red via vector extrapolation"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "hybrid approximate message passing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

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)