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: "blind image deconvolution using deep generative priors"
  Abstract: This paper proposes a novel approach to regularize the \textit{ill-posed} and \textit{non-linear} blind image deconvolution (blind deblurring) using deep generative networks as priors. We employ two separate generative models --- one trained to produce sharp images while the other trained to generate blur kernels from lower-dimensional parameters. To deblur, we propose an alternating gradient descent scheme operating in the latent lower-dimensional space of each of the pretrained generative models. Our experiments show promising deblurring results on images even under large blurs, and heavy noise. To address the shortcomings of generative models such as mode collapse, we augment our generative priors with classical image priors and report improved performance on complex image datasets. The deblurring performance depends on how well the range of the generator spans the image class. Interestingly, our experiments show that even an untrained structured (convolutional) generative networks acts as an image prior in the image deblurring context allowing us to extend our results to more diverse natural image datasets.

Connected through hyperedge HE-1 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models"):
  with members:
    - "deep compressed sensing"
    - "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"
    - "invertible generative models for inverse problems mitigating representation error and dataset bias"
    - "fast and provable admm for learning with generative priors"
    - "blind phaseless short time fourier transform recovery"
    - "deep ptych subsampled fourier ptychography using generative priors"
    - "phase retrieval under a generative prior"
    - "robust compressive phase retrieval via deep generative priors"

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"
    - "learning collaborative generation correction modules for blind image deblurring and beyond"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "motion deblurring in the wild"

Connected through hyperedge HE-3 (induced by source paper "invertible generative models for inverse problems mitigating representation error and dataset bias"):
  with members:
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "analyzing inverse problems with invertible neural networks"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "finger gan generating realistic fingerprint images using connectivity imposed gan"
    - "latent convolutional models"
    - "phase retrieval under a generative prior"
    - "i revnet deep invertible networks"
    - "the reversible residual network backpropagation without storing activations"

Connected through hyperedge HE-4 (induced by source paper "low shot learning with untrained neural networks for imaging inverse problems"):
  with members:
    - "deep compressed sensing"
    - "algorithmic guarantees for inverse imaging with untrained network priors"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "phase retrieval under a generative prior"
    - "robust compressive phase retrieval via deep generative priors"
    - "deep image prior"

Connected through hyperedge HE-5 (induced by source paper "computational mirrors blind inverse light transport by deep matrix factorization"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "neural blind deconvolution using deep priors"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep image prior"
    - "optically lightweight tracking of objects around a corner"
    - "a deep matrix factorization method for learning attribute representations"
    - "adam a method for stochastic optimization"

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)