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: "an all in one network for dehazing and beyond"
  Abstract: This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level task performance on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN and training the joint pipeline from end to end, we witness a large improvement of the object detection performance on hazy images.

Connected through hyperedge HE-1 (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-2 (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"

Connected through hyperedge HE-3 (induced by source paper "densely connected pyramid dehazing network"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "gated fusion network for single image dehazing"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "residual dense network for image super resolution"
    - "context encoding for semantic segmentation"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "high quality facial photo sketch synthesis using multi adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "residual guide feature fusion network for single image deraining"):
  with members:
    - "density aware single image de raining using a multi stream dense network"
    - "densely connected pyramid dehazing network"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "multi scale dense networks for resource efficient image classification"
    - "image de raining using a conditional generative adversarial network"
    - "memnet a persistent memory network for image restoration"
    - "dehazenet an end to end system for single image haze removal"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-5 (induced by source paper "fully point wise convolutional neural network for modeling statistical regularities in natural images"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "dehazenet an end to end system for single image haze removal"
    - "fast fourier color constancy"
    - "convolutional color constancy"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "improved techniques for learning to dehaze and beyond a collective study"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "densely connected pyramid dehazing network"
    - "end to end united video dehazing and detection"
    - "focal loss for dense object detection"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "benchmarking single image dehazing and beyond"
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"

Connected through hyperedge HE-7 (induced by source paper "does haze removal help cnn based image classification"):
  with members:
    - "residual attention network for image classification"
    - "dehazenet an end to end system for single image haze removal"
    - "joint visual denoising and classification using deep learning"
    - "cnn rnn a unified framework for multi label image classification"
    - "studying very low resolution recognition using deep networks"
    - "deep residual learning for image recognition"
    - "understanding deep image representations by inverting them"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "generic model agnostic convolutional neural networks for single image dehazing"):
  with members:
    - "gated fusion network for single image dehazing"
    - "benchmarking single image dehazing and beyond"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "dehazenet an end to end system for single image haze removal"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "accurate image super resolution using very deep convolutional networks"
    - "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)