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: "cascades of regression tree fields for image restoration"
  Abstract: Conditional random fields (CRFs) are popular discriminative models for computer vision and have been successfully applied in the domain of image restoration, especially to image denoising. For image deblurring, however, discriminative approaches have been mostly lacking. We posit two reasons for this: First, the blur kernel is often only known at test time, requiring any discriminative approach to cope with considerable variability. Second, given this variability it is quite difficult to construct suitable features for discriminative prediction. To address these challenges we first show a connection between common half-quadratic inference for generative image priors and Gaussian CRFs. Based on this analysis, we then propose a cascade model for image restoration that consists of a Gaussian CRF at each stage. Each stage of our cascade is semi-parametric, i.e., it depends on the instance-specific parameters of the restoration problem, such as the blur kernel. We train our model by loss minimization with synthetically generated training data. Our experiments show that when applied to non-blind image deblurring, the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur. Moreover, we demonstrate its suitability for image denoising, where we achieve competitive results for grayscale and color images.

Connected through hyperedge HE-1 (induced by source paper "learning deep gradient descent optimization for image deconvolution"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "learning aggregated transmission propagation networks for haze removal and beyond"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-2 (induced by source paper "invert to learn to invert"):
  with members:
    - "analyzing inverse problems with invertible neural networks"
    - "invertible residual networks"
    - "i revnet deep invertible networks"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "the reversible residual network backpropagation without storing activations"
    - "recurrent inference machines for solving inverse problems"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-3 (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"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "deep autoencoder for combined human pose estimation and body model upscaling"):
  with members:
    - "multimedia super resolution via deep learning a survey"
    - "volumetric super resolution of multispectral data"
    - "lstm pose machines"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "recurrent 3d pose sequence machines"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "learning to fuse 2d and 3d image cues for monocular body pose estimation"

Connected through hyperedge HE-5 (induced by source paper "simultaneous fidelity and regularization learning for image restoration"):
  with members:
    - "learning to deblur images with exemplars"
    - "blade filter learning for general purpose computational photography"
    - "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"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "deep joint rain detection and removal from a single image"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-6 (induced by source paper "learning collaborative generation correction modules for blind image deblurring and beyond"):
  with members:
    - "toward designing convergent deep operator splitting methods for task specific nonconvex optimization"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "proximal alternating direction network a globally converged deep unrolling framework"
    - "learning deep cnn denoiser prior for image restoration"
    - "learning fully convolutional networks for iterative non blind deconvolution"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "beyond a gaussian denoiser residual learning of deep cnn for image denoising"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "compression artifacts reduction by a deep convolutional network"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "learning aggregated transmission propagation networks for haze removal and beyond"):
  with members:
    - "toward designing convergent deep operator splitting methods for task specific nonconvex optimization"
    - "proximal alternating direction network a globally converged deep unrolling framework"
    - "dehazenet an end to end system for single image haze removal"
    - "identity mappings in deep residual networks"
    - "energy based generative adversarial network"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "haze visibility enhancement a survey and quantitative benchmarking"
    - "deep residual learning for image recognition"

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)