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: "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"
  Abstract: A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP-EM algorithm. A dual mathematical interpretation of the proposed framework with structured sparse estimation is described, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared to traditional sparse inverse problem techniques. This interpretation also suggests an effective dictionary motivated initialization for the MAP-EM algorithm. We demonstrate that in a number of image inverse problems, including inpainting, zooming, and deblurring, the same algorithm produces either equal, often significantly better, or very small margin worse results than the best published ones, at a lower computational cost.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "joint demosaicing and denoising by overfitting of bursts of raw images"):
  with members:
    - "handheld multi frame super resolution"
    - "learning to adapt for stereo"
    - "noise2self blind denoising by self supervision"
    - "real time self adaptive deep stereo"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "learning to see in the dark"

Connected through hyperedge HE-3 (induced by source paper "joint demosaicking and denoising by fine tuning of bursts of raw images"):
  with members:
    - "learning to adapt for stereo"
    - "noise2self blind denoising by self supervision"
    - "real time self adaptive deep stereo"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-4 (induced by source paper "learning converged propagations with deep prior ensemble for image enhancement"):
  with members:
    - "clearing the skies a deep network architecture for single image rain removal"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "dehazenet an end to end system for single image haze removal"
    - "deep joint rain detection and removal from a single image"
    - "haze visibility enhancement a survey and quantitative benchmarking"
    - "cascades of regression tree fields for image restoration"

Connected through hyperedge HE-5 (induced by source paper "blind visual motif removal from a single image"):
  with members:
    - "generative single image reflection separation"
    - "single image reflection separation with perceptual losses"
    - "generative image inpainting with contextual attention"
    - "image restoration by iterative denoising and backward projections"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "deep blind image inpainting"
    - "image de raining using a conditional generative adversarial network"
    - "xunit learning a spatial activation function for efficient image restoration"

Connected through hyperedge HE-6 (induced by source paper "three dimensional optical coherence tomography image denoising through multi input fully convolutional networks"):
  with members:
    - "on the relation between color image denoising and classification"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "fast image processing with fully convolutional networks"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "learned svd solving inverse problems via hybrid autoencoding"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "joint group and residual sparse coding for image compressive sensing"):
  with members:
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "group based sparse representation for image compressive sensing reconstruction with non convex regularization"
    - "group based sparse representation for image restoration"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "structural group sparse representation for image compressive sensing recovery"

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)