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: "dehazenet an end to end system for single image haze removal"
  Abstract: Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, the layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called bilateral rectified linear unit, which is able to improve the quality of recovered haze-free image. We establish connections between the components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use.

Connected through hyperedge HE-1 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robust video content alignment and compensation for clear vision through the rain"
    - "residual dense network for image super resolution"
    - "o haze a dehazing benchmark with real hazy and haze free outdoor images"

Connected through hyperedge HE-2 (induced by source paper "simultaneous enhancement and super resolution of underwater imagery for improved visual perception"):
  with members:
    - "real time model based image color correction for underwater robots"
    - "deep learning for single image super resolution a brief review"
    - "feedback network for image super resolution"
    - "fast underwater image enhancement for improved visual perception"
    - "an underwater image enhancement benchmark dataset and beyond"
    - "deep visual attention prediction"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "fast and accurate single image super resolution via information distillation network"

Connected through hyperedge HE-3 (induced by source paper "blind image restoration without prior knowledge"):
  with members:
    - "blind universal bayesian image denoising with gaussian noise level learning"
    - "blind super resolution with iterative kernel correction"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "deep network interpolation for continuous imagery effect transition"

Connected through hyperedge HE-4 (induced by source paper "famed net a fast and accurate multi scale end to end dehazing network"):
  with members:
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "fully point wise convolutional neural network for modeling statistical regularities in natural images"
    - "semantic foggy scene understanding with synthetic data"
    - "lapran a scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction"
    - "gated fusion network for single image dehazing"
    - "deep retinex decomposition for low light enhancement"
    - "densely connected pyramid dehazing network"
    - "end to end united video dehazing and detection"

Connected through hyperedge HE-5 (induced by source paper "domain adaptive adversarial learning based on physics model feedback for underwater image enhancement"):
  with members:
    - "an underwater image enhancement benchmark dataset and beyond"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "emerging from water underwater image color correction based on weakly supervised color transfer"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "enhancing underwater imagery using generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image de raining using a conditional generative adversarial network"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "fd gan generative adversarial networks with fusion discriminator for single image dehazing"):
  with members:
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "gated fusion network for single image dehazing"
    - "residual dense network for image super resolution"
    - "megadepth learning single view depth prediction from internet photos"
    - "densely connected pyramid dehazing network"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "image de raining using a conditional generative adversarial network"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "joint enhancement and denoising method via sequential decomposition"
    - "getting to know low light images with the exclusively dark dataset"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-8 (induced by source paper "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "gated2depth real time dense lidar from gated images"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "robust video content alignment and compensation for clear vision through the rain"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

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)