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: "end to end united video dehazing and detection"
  Abstract: The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been explored yet. In this paper, we propose an End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal consistency between consecutive video frames. A thorough study has been conducted over a number of structure options, to identify the best temporal fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model. The resulting augmented end-to-end pipeline has demonstrated much more stable and accurate detection results in hazy video.

Connected through hyperedge HE-1 (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"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

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 "positional normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "pay less attention with lightweight and dynamic convolutions"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "differentiable learning to normalize via switchable normalization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "domain adaptive faster r cnn for object detection in the wild"

Connected through hyperedge HE-5 (induced by source paper "single image deraining a comprehensive benchmark analysis"):
  with members:
    - "bridging the gap between computational photography and visual recognition"
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "does haze removal help cnn based image classification"
    - "yolov3 an incremental improvement"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "density aware single image de raining using a multi stream dense network"
    - "focal loss for dense object detection"
    - "when image denoising meets high level vision tasks a deep learning approach"

Connected through hyperedge HE-6 (induced by source paper "all in one underwater image enhancement using domain adversarial learning"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "an underwater image enhancement benchmark dataset and beyond"
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "deep underwater image enhancement"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-7 (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"
    - "focal loss for dense object detection"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "benchmarking single image dehazing and beyond"
    - "an all in one network for dehazing and beyond"
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"

Connected through hyperedge HE-8 (induced by source paper "benchmarking single image dehazing and beyond"):
  with members:
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "semantic foggy scene understanding with synthetic data"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "dehazenet an end to end system for single image haze removal"

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)