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: "one network to solve them all solving linear inverse problems using deep projection models"
  Abstract: While deep learning methods have achieved state-of-the-art performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks. Under this approach, different problems require different networks. In scenarios where we need to solve a wide variety of problems, e.g., on a mobile camera, it is inefficient and costly to use these specially-trained networks. On the other hand, traditional methods using signal priors can be used in all linear inverse problems but often have worse performance on challenging tasks. In this work, we provide a middle ground between the two kinds of methods --- we propose a general framework to train a single deep neural network that solves arbitrary linear inverse problems. The proposed network acts as a proximal operator for an optimization algorithm and projects non-image signals onto the set of natural images defined by the decision boundary of a classifier. In our experiments, the proposed framework demonstrates superior performance over traditional methods using a wavelet sparsity prior and achieves comparable performance of specially-trained networks on tasks including compressive sensing and pixel-wise inpainting.

Connected through hyperedge HE-1 (induced by source paper "proximal splitting networks for image restoration"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deep mean shift priors for image restoration"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "accelerating the super resolution convolutional neural network"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "training auto encoder based optimizers for terahertz image reconstruction"):
  with members:
    - "controlling neural networks via energy dissipation"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "deep image prior"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "optnet differentiable optimization as a layer in neural networks"
    - "benchmarking denoising algorithms with real photographs"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-3 (induced by source paper "mimicgan corruption mimicking for blind image recovery adversarial defense"):
  with members:
    - "universal decision based black box perturbations breaking security through obscurity defenses"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "the robust manifold defense adversarial training using generative models"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "lose the views limited angle ct reconstruction via implicit sinogram completion"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "solving inverse computational imaging problems using deep pixel level prior"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "lensless computational imaging through deep learning"
    - "learning deep cnn denoiser prior for image restoration"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "compressive image recovery using recurrent generative model"

Connected through hyperedge HE-5 (induced by source paper "compressive light field reconstructions using deep learning"):
  with members:
    - "deep fully connected networks for video compressive sensing"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "learning based view synthesis for light field cameras"
    - "context encoders feature learning by inpainting"
    - "learning sensor multiplexing design through back propagation"
    - "a deep learning approach to structured signal recovery"

Connected through hyperedge HE-6 (induced by source paper "nonlocal low rank tensor factor analysis for image restoration"):
  with members:
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "block wise lensless compressive camera"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "a deep learning approach to structured signal recovery"
    - "lensless compressive imaging"
    - "compressive hyperspectral imaging with side information"

Connected through hyperedge HE-7 (induced by source paper "an unsupervised approach to solving inverse problems using generative adversarial networks"):
  with members:
    - "deep image prior"
    - "lose the views limited angle ct reconstruction via implicit sinogram completion"
    - "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"
    - "context encoders feature learning by inpainting"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "cnn based projected gradient descent for consistent ct image reconstruction"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "deep learning with domain adaptation for accelerated projection reconstruction mr"
    - "deep learning for photoacoustic tomography from sparse data"
    - "recurrent inference machines for solving inverse problems"
    - "skip connections as effective symmetry breaking"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"

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)