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: "rate optimal denoising with deep neural networks"
  Abstract: Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy observation. The underlying principle is that neural networks trained on large datasets have empirically been shown to be able to generate natural images well from a low-dimensional latent representation of the image. Given such a generator network, a noisy image can be denoised by i) finding the closest image in the range of the generator or by ii) passing it through an encoder-generator architecture (known as an autoencoder). However, there is little theory to justify this success, let alone to predict the denoising performance as a function of the network parameters. In this paper we consider the problem of denoising an image from additive Gaussian noise using the two generator based approaches. In both cases, we assume the image is well described by a deep neural network with ReLU activations functions, mapping a $k$-dimensional code to an $n$-dimensional image. In the case of the autoencoder, we show that the feedforward network reduces noise energy by a factor of $O(k/n)$. In the case of optimizing over the range of a generative model, we state and analyze a simple gradient algorithm that minimizes a non-convex loss function, and provably reduces noise energy by a factor of $O(k/n)$. 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 "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"
    - "deep decoder concise image representations from untrained non convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "inverting deep generative models one layer at a time"):
  with members:
    - "asymptotics of map inference in deep 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"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "correction by projection denoising images with generative adversarial networks"

Connected through hyperedge HE-3 (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"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "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-4 (induced by source paper "subgradient descent learns orthogonal dictionaries"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "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"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"

Connected through hyperedge HE-5 (induced by source paper "algorithmic aspects of inverse problems using generative models"):
  with members:
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "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"

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)