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: "joint transmission map estimation and dehazing using deep networks"
  Abstract: Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed in the literature to solve this problem and they have achieved superior results. However, most of the existing methods assume constant atmospheric light model and tend to follow a two- step procedure involving prior-based methods for estimating transmission map followed by calculation of dehazed image using the closed form solution. In this paper, we relax the constant atmospheric light assumption and propose a novel unified single image dehazing network that jointly estimates the transmission map and performs dehazing. In other words, our new approach provides an end-to-end learning framework, where the inherent transmission map and dehazed result are learned directly from the loss function. Extensive experiments on synthetic and real datasets with challenging hazy images demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "gated fusion network for degraded image super resolution"):
  with members:
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"):
  with members:
    - "real time 3d traffic cone detection for autonomous driving"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "deepblindness fast blindness map estimation and blindness type classification for outdoor scene from single color image"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning to synthesize motion blur"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "haze density estimation via modeling of scattering coefficients of iso depth regions"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
    - "a unified approach of multi scale deep and hand crafted features for defocus estimation"
    - "sparsity invariant cnns"

Connected through hyperedge HE-4 (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-5 (induced by source paper "density aware single image de raining using a multi stream dense network"):
  with members:
    - "residual dense network for image super resolution"
    - "context encoding for semantic segmentation"
    - "densely connected pyramid dehazing network"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "multi style generative network for real time transfer"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"

Connected through hyperedge HE-6 (induced by source paper "gp gan gender preserving gan for synthesizing faces from landmarks"):
  with members:
    - "every smile is unique landmark guided diverse smile generation"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "densely connected pyramid dehazing network"
    - "began boundary equilibrium generative adversarial networks"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "pose guided person image generation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "natural and effective obfuscation by head inpainting"

Connected through hyperedge HE-7 (induced by source paper "model adaptation with synthetic and real data for semantic dense foggy scene understanding"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "learning driving models with a surround view camera system and a route planner"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "semantic foggy scene understanding with synthetic data"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "cycada cycle consistent adversarial domain adaptation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "playing for data ground truth from computer games"
    - "pyramid scene parsing network"

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)