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: "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
  Abstract: Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination. To cope with these challenges, we design a cascaded network architecture that unrolls the proximal gradient iterations by permeating benefits from generative residual networks (ResNet) to modeling the proximal operator. A mixture of pixel-wise and perceptual costs is then deployed to train proximals. The overall architecture resembles back-and-forth projection onto the intersection of feasible and plausible images. Extensive computational experiments are examined for a global task of reconstructing MR images of pediatric patients, and a more local task of superresolving CelebA faces, that are insightful to design efficient architectures. Our observations indicate that for MRI reconstruction, a recurrent ResNet with a single residual block effectively learns the proximal. This simple architecture appears to significantly outperform the alternative deep ResNet architecture by 2dB SNR, and the conventional compressed-sensing MRI by 4dB SNR with 100x faster inference. For image superresolution, our preliminary results indicate that modeling the denoising proximal demands deep ResNets.

Connected through hyperedge HE-1 (induced by source paper "modl model based deep learning architecture for inverse problems"):
  with members:
    - "model based free breathing cardiac mri reconstruction using deep learned storm priors modl storm"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "unrolled optimization with deep priors"
    - "recurrent inference machines for solving inverse problems"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"

Connected through hyperedge HE-2 (induced by source paper "mr image reconstruction using deep density priors"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "image reconstruction by domain transform manifold learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "tackling over pruning in variational autoencoders"
    - "deep convolutional neural network for inverse problems in imaging"
    - "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"
    - "a survey on deep learning in medical image analysis"
    - "recurrent inference machines for solving inverse problems"

Connected through hyperedge HE-3 (induced by source paper "recent advances in recurrent neural networks"):
  with members:
    - "gated orthogonal recurrent units on learning to forget"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "interpretation of mammogram and chest radiograph reports using deep neural networks preliminary results"
    - "recurrent dropout without memory loss"
    - "recurrent memory network for language modeling"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

Connected through hyperedge HE-4 (induced by source paper "learning to synthesize splitting and recombining low and high spatial frequencies for image recovery"):
  with members:
    - "learning dual convolutional neural networks for low level vision"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-5 (induced by source paper "task adapted reconstruction for inverse problems"):
  with members:
    - "neural proximal gradient descent for compressive imaging"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "on calibration of modern neural networks"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "learning to solve inverse problems using wasserstein loss"
    - "perceptual losses for real time style transfer and super resolution"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "rate optimal denoising with deep neural networks"):
  with members:
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "why are deep nets reversible a simple theory with implications for training"

Connected through hyperedge HE-7 (induced by source paper "synergistic reconstruction and synthesis via generative adversarial networks for accelerated multi contrast mri"):
  with members:
    - "instance normalization the missing ingredient for fast stylization"
    - "deep residual learning for compressed sensing ct reconstruction via persistent homology analysis"

Connected through hyperedge HE-8 (induced by source paper "global guarantees for enforcing deep generative priors by empirical risk"):
  with members:
    - "how generative adversarial networks and their variants work an overview"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "phase retrieval under a generative prior"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "a convergence theory for deep learning via over parameterization"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

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.AI(Artificial Intelligence)