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: "o haze a dehazing benchmark with real hazy and haze free outdoor images"
  Abstract: Haze removal or dehazing is a challenging ill-posed problem that has drawn a significant attention in the last few years. Despite this growing interest, the scientific community is still lacking a reference dataset to evaluate objectively and quantitatively the performance of proposed dehazing methods. The few datasets that are currently considered, both for assessment and training of learning-based dehazing techniques, exclusively rely on synthetic hazy images. To address this limitation, we introduce the first outdoor scenes database (named O-HAZE) composed of pairs of real hazy and corresponding haze-free images. In practice, hazy images have been captured in presence of real haze, generated by professional haze machines, and OHAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. To illustrate its usefulness, O-HAZE is used to compare a representative set of state-of-the-art dehazing techniques, using traditional image quality metrics such as PSNR, SSIM and CIEDE2000. This reveals the limitations of current techniques, and questions some of their underlying assumptions.

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"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "feature forwarding for efficient single image dehazing"):
  with members:
    - "dense haze a benchmark for image dehazing with dense haze and haze free images"
    - "densely connected pyramid dehazing network"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "dehazenet an end to end system for single image haze removal"
    - "image to image translation with conditional adversarial networks"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-5 (induced by source paper "single image haze removal using conditional wasserstein generative adversarial networks"):
  with members:
    - "improved training of wasserstein gans"
    - "dehazenet an end to end system for single image haze removal"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "fast single image dehazing via multilevel wavelet transform based optimization"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "dehazenet an end to end system for single image haze removal"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"

Connected through hyperedge HE-7 (induced by source paper "dense haze a benchmark for image dehazing with dense haze and haze free images"):
  with members:
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "dehazenet an end to end system for single image haze removal"

Connected through hyperedge HE-8 (induced by source paper "the effectiveness of instance normalization a strong baseline for single image dehazing"):
  with members:
    - "a cascaded convolutional neural network for single image dehazing"
    - "semantic foggy scene understanding with synthetic data"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "semantic single image dehazing"
    - "densely connected pyramid dehazing network"
    - "deep image prior"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "benchmarking single image dehazing and beyond"

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)