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: "modl model based deep learning architecture for inverse problems"
  Abstract: We introduce a model-based image reconstruction framework with a convolution neural network (CNN)-based regularization prior. The proposed formulation provides a systematic approach for deriving deep architectures for inverse problems with the arbitrary structure. Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to direct inversion approaches. Thus, reducing the demand for training data and training time. Since we rely on end-to-end training with weight sharing across iterations, the CNN weights are customized to the forward model, thus offering improved performance over approaches that rely on pre-trained denoisers. Our experiments show that the decoupling of the number of iterations from the network complexity offered by this approach provides benefits, including lower demand for training data, reduced risk of overfitting, and implementations with significantly reduced memory footprint. We propose to enforce data-consistency by using numerical optimization blocks, such as conjugate gradients algorithm within the network. This approach offers faster convergence per iteration, compared to methods that rely on proximal gradients steps to enforce data consistency. Our experiments show that the faster convergence translates to improved performance, primarily when the available GPU memory restricts the number of iterations.

Connected through hyperedge HE-1 (induced by source paper "neumann networks for inverse problems in imaging"):
  with members:
    - "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"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "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-2 (induced by source paper "optimal transport cyclegan and penalized ls for unsupervised learning in inverse problems"):
  with members:
    - "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
    - "deep compressed sensing"
    - "k space deep learning for accelerated mri"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "mr image reconstruction using deep density priors"):
  with members:
    - "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 generative adversarial networks for proximal learning and automated compressive image recovery"
    - "recurrent inference machines for solving inverse problems"

Connected through hyperedge HE-4 (induced by source paper "ifr net iterative feature refinement network for compressed sensing mri"):
  with members:
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "image reconstruction by domain transform manifold learning"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "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-5 (induced by source paper "deep generalization of structured low rank algorithms deep slr"):
  with members:
    - "structured low rank algorithms theory mr applications and links to machine learning"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "off the grid model based deep learning o modl"
    - "k space deep learning for accelerated mri"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "super resolution mri using finite rate of innovation curves"

Connected through hyperedge HE-6 (induced by source paper "self supervised deep active accelerated mri"):
  with members:
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "unified theory for recovery of sparse signals in a general transform domain"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "monte carlo tree search with heuristic evaluations using implicit minimax backups"

Connected through hyperedge HE-7 (induced by source paper "crdn cascaded residual dense networks for dynamic mr imaging with edge enhanced loss constraint"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "residual dense network for image super resolution"
    - "image reconstruction by domain transform manifold learning"
    - "convolutional recurrent neural networks for dynamic mr image reconstruction"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "tensorflow a system for large scale machine learning"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "learned svd solving inverse problems via hybrid autoencoding"):
  with members:
    - "optimal transport cyclegan and penalized ls for unsupervised learning in inverse problems"
    - "approximate k space models and deep learning for fast photoacoustic reconstruction"
    - "motion blur removal via coupled autoencoder"
    - "deep convolutional neural network for inverse problems in imaging"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "representation learning a review and new perspectives"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

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)