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: "cnn based projected gradient descent for consistent ct image reconstruction"
  Abstract: We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism to enforce that the reconstructed image is consistent with the measurements. We propose a relaxed version of PGD wherein gradient descent enforces measurement consistency, while a CNN recursively projects the solution closer to the space of desired reconstruction images. We show that this algorithm is guaranteed to converge and, under certain conditions, converges to a local minimum of a non-convex inverse problem. Finally, we propose a simple scheme to train the CNN to act like a projector. Our experiments on sparse-view computed-tomography reconstruction show an improvement over total variation-based regularization, dictionary learning, and a state-of-the-art deep learning-based direct reconstruction technique.

Connected through hyperedge HE-1 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"
    - "learning the invisible a hybrid deep learning shearlet framework for limited angle computed tomography"
    - "adversarial attacks and defences a survey"
    - "image reconstruction by domain transform manifold learning"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-2 (induced by source paper "deep neural networks with trainable activations and controlled lipschitz constant"):
  with members:
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "exploring generalization in deep learning"
    - "deep convolutional neural network for inverse problems in imaging"
    - "understanding deep neural networks with rectified linear units"
    - "u net convolutional networks for biomedical image segmentation"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning activation functions to improve deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "neumann networks for inverse problems in imaging"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "neural proximal gradient descent for compressive imaging"
    - "off the grid model based deep learning o modl"
    - "multi shot sensitivity encoded diffusion mri using model based deep learning modl mussels"
    - "neural ordinary differential equations"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-4 (induced by source paper "deep decomposition learning for inverse imaging problems"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "deep image prior"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "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"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-5 (induced by source paper "gan based projector for faster recovery with convergence guarantees in linear inverse problems"):
  with members:
    - "a gpu oriented algorithm design for secant based dimensionality reduction"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "began boundary equilibrium generative adversarial networks"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "the little engine that could regularization by denoising red"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-6 (induced by source paper "ifr net iterative feature refinement network for compressed sensing mri"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "image reconstruction by domain transform manifold learning"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "deep convolutional neural network for inverse problems in imaging"
    - "convolutional recurrent neural networks for dynamic mr image reconstruction"
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-7 (induced by source paper "accelerating optimization algorithms with dynamic parameter selections using convolutional neural networks for inverse problems in image processing"):
  with members:
    - "plug and play methods provably converge with properly trained denoisers"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "learning to optimize"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "u net convolutional networks for biomedical image segmentation"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "computationally efficient deep neural network for computed tomography image reconstruction"):
  with members:
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)