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: "proximal alternating direction network a globally converged deep unrolling framework"
  Abstract: Deep learning models have gained great success in many real-world applications. However, most existing networks are typically designed in heuristic manners, thus lack of rigorous mathematical principles and derivations. Several recent studies build deep structures by unrolling a particular optimization model that involves task information. Unfortunately, due to the dynamic nature of network parameters, their resultant deep propagation networks do \emph{not} possess the nice convergence property as the original optimization scheme does. This paper provides a novel proximal unrolling framework to establish deep models by integrating experimentally verified network architectures and rich cues of the tasks. More importantly, we \emph{prove in theory} that 1) the propagation generated by our unrolled deep model globally converges to a critical-point of a given variational energy, and 2) the proposed framework is still able to learn priors from training data to generate a convergent propagation even when task information is only partially available. Indeed, these theoretical results are the best we can ask for, unless stronger assumptions are enforced. Extensive experiments on various real-world applications verify the theoretical convergence and demonstrate the effectiveness of designed deep models.

Connected through hyperedge HE-1 (induced by source paper "image stylization from predefined to personalized"):
  with members:
    - "blade filter learning for general purpose computational photography"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "perceptual losses for real time style transfer and super resolution"
    - "controlling perceptual factors in neural style transfer"

Connected through hyperedge HE-2 (induced by source paper "deep plug and play super resolution for arbitrary blur kernels"):
  with members:
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "gated fusion network for joint image deblurring and super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "regularization by denoising clarifications and new interpretations"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "neural nearest neighbors networks"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"

Connected through hyperedge HE-3 (induced by source paper "on the convergence of admm with task adaption and beyond"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "regularization by denoising clarifications and new interpretations"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "density aware single image de raining using a multi stream dense network"
    - "a theoretically guaranteed deep optimization framework for robust compressive sensing mri"
    - "toward designing convergent deep operator splitting methods for task specific nonconvex optimization"
    - "clearing the skies a deep network architecture for single image rain removal"

Connected through hyperedge HE-4 (induced by source paper "underexposed image correction via hybrid priors navigated deep propagation"):
  with members:
    - "kindling the darkness a practical low light image enhancer"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "yolov3 an incremental improvement"
    - "deep retinex decomposition for low light enhancement"
    - "residual dense network for image super resolution"
    - "learning collaborative generation correction modules for blind image deblurring and beyond"
    - "deep image prior"

Connected through hyperedge HE-5 (induced by source paper "hyperadam a learnable task adaptive adam for network training"):
  with members:
    - "on the convergence of adam and beyond"
    - "one shot visual imitation learning via meta learning"
    - "prototypical networks for few shot learning"
    - "learning gradient descent better generalization and longer horizons"
    - "learned optimizers that scale and generalize"
    - "listening to the world improves speech command recognition"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"

Connected through hyperedge HE-6 (induced by source paper "a flexible convolutional solver with application to photorealistic style transfer"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "neural color transfer between images"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deep photo style transfer"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "stylebank an explicit representation for neural image style transfer"

Connected through hyperedge HE-7 (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"
    - "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"
    - "cascades of regression tree fields for image restoration"
    - "you only look once unified real time object detection"

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"
    - "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"
    - "cascades of regression tree fields for image restoration"
    - "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)