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: "deep denoising rate optimal recovery of structured signals with a deep prior"
  Abstract: Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to map a noisy image to a near noise-free image. The underlying principle is simple: images are well described by priors that map a low-dimensional latent representations to image. Based on a prior, a noisy image can be denoised by finding a close image in the range of the prior. Since deep networks trained on large set of images have empirically been shown to be good priors, they enable effective denoisers. However, there is little theory to justify this success, let alone to predict the denoising performance. In this paper we consider the problem of denoising an image from additive Gaussian noise with variance $\sigma^2$, assuming the image is well described by a deep neural network with ReLu activations functions, mapping a $k$-dimensional latent space to an $n$-dimensional image. We provide an iterative algorithm minimizing a non-convex loss that provably removes noise energy by a fraction $\sigma^2 k/n$. We also demonstrate in numerical experiments that this denoising performance is, indeed, achieved by generative priors learned from data.

Connected through hyperedge HE-1 (induced by source paper "fast and provable admm for learning with generative priors"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "algorithmic aspects of inverse problems using generative models"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "phase retrieval under a generative prior"
    - "large scale gan training for high fidelity natural image synthesis"
    - "adversarial audio synthesis"
    - "correction by projection denoising images with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "denoising and regularization via exploiting the structural bias of convolutional generators"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "algorithmic guarantees for inverse imaging with untrained network priors"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "rate optimal denoising with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "inverting deep generative models one layer at a time"):
  with members:
    - "asymptotics of map inference in deep networks"
    - "rate optimal denoising with deep neural networks"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "random mesh projectors for inverse problems"
    - "neural proximal gradient descent for compressive imaging"
    - "sunlayer stable denoising with generative networks"
    - "phase retrieval under a generative prior"
    - "correction by projection denoising images with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "regularizing linear inverse problems with convolutional neural networks"):
  with members:
    - "algorithmic guarantees for inverse imaging with untrained network priors"
    - "channel normalization in convolutional neural network avoids vanishing gradients"
    - "rate optimal denoising with deep neural networks"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "deep image prior"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-5 (induced by source paper "subgradient descent learns orthogonal dictionaries"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "rate optimal denoising with deep neural networks"
    - "nonconvex robust low rank matrix recovery"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "learning relu networks via alternating minimization"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"

Connected through hyperedge HE-6 (induced by source paper "algorithmic aspects of inverse problems using generative models"):
  with members:
    - "rate optimal denoising with deep neural networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-7 (induced by source paper "deep decoder concise image representations from untrained non convolutional networks"):
  with members:
    - "phase retrieval under a generative prior"
    - "robust compressive phase retrieval via deep generative priors"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "the little engine that could regularization by denoising red"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "variable rate image compression with recurrent neural networks"

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.IT(Information Theory)