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: "correction by projection denoising images with generative adversarial networks"
  Abstract: Generative adversarial networks (GANs) transform low-dimensional latent vectors into visually plausible images. If the real dataset contains only clean images, then ostensibly, the manifold learned by the GAN should contain only clean images. In this paper, we propose to denoise corrupted images by finding the nearest point on the GAN manifold, recovering latent vectors by minimizing distances in image space. We first demonstrate that given a corrupted version of an image that truly lies on the GAN manifold, we can approximately recover the latent vector and denoise the image, obtaining significantly higher quality, comparing with BM3D. Next, we demonstrate that latent vectors recovered from noisy images exhibit a consistent bias. By subtracting this bias before projecting back to image space, we improve denoising results even further. Finally, even for unseen images, our method performs better at denoising better than BM3D. Notably, the basic version of our method (without bias correction) requires no prior knowledge on the noise variance. To achieve the highest possible denoising quality, the best performing signal processing based methods, such as BM3D, require an estimate of the blur kernel.

Connected through hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "inference with deep generative priors in high dimensions"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "sunlayer stable denoising with generative networks"
    - "task aware compressed sensing with generative adversarial networks"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "deep image prior"

Connected through hyperedge HE-2 (induced by source paper "inference with deep generative priors in high dimensions"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "inverting deep generative models one layer at a time"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "sunlayer stable denoising with generative networks"

Connected through hyperedge HE-3 (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"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "large scale gan training for high fidelity natural image synthesis"
    - "adversarial audio synthesis"

Connected through hyperedge HE-4 (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"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"

Connected through hyperedge HE-5 (induced by source paper "an approach to image denoising using manifold approximation without clean images"):
  with members:
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "noise2noise learning image restoration without clean data"
    - "denoising adversarial autoencoders"
    - "deep image prior"
    - "perceptual losses for real time style transfer and super resolution"
    - "medical image denoising using convolutional denoising autoencoders"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generative visual manipulation on the natural image manifold"

Connected through hyperedge HE-6 (induced by source paper "deep learning for image denoising a survey"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "group normalization"
    - "a variational u net for conditional appearance and shape generation"
    - "residual dense network for image super resolution"
    - "dilated deep residual network for image denoising"
    - "learning a single convolutional super resolution network for multiple degradations"

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)