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: "improved techniques for learning to dehaze and beyond a collective study"
  Abstract: Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e.g., object detection) of hazy images. For the first task, we investigated a variety of loss functions and show that perception-driven loss significantly improves dehazing performance. In the second task, we provide multiple solutions including using advanced modules in the dehazing-detection cascade and domain-adaptive object detectors. In both tasks, our proposed solutions significantly improve performance. GitHub repository URL is: this https URL

Connected through hyperedge HE-1 (induced by source paper "famed net a fast and accurate multi scale end to end dehazing network"):
  with members:
    - "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"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-2 (induced by source paper "enlightengan deep light enhancement without paired supervision"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "single image deraining a comprehensive benchmark analysis"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "joint enhancement and denoising method via sequential decomposition"
    - "the relativistic discriminator a key element missing from standard gan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "scale recurrent network for deep image deblurring"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "single image deraining a comprehensive benchmark analysis"):
  with members:
    - "bridging the gap between computational photography and visual recognition"
    - "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"
    - "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"

Connected through hyperedge HE-4 (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"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "deep underwater image enhancement"
    - "yolov3 an incremental improvement"
    - "enhancing underwater imagery using generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "u finger multi scale dilated convolutional network for fingerprint image denoising and inpainting"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "studying very low resolution recognition using deep networks"
    - "multi scale context aggregation by dilated convolutions"
    - "learning super resolution jointly from external and internal examples"

Connected through hyperedge HE-6 (induced by source paper "benchmarking single image dehazing and beyond"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "end to end united video dehazing and detection"
    - "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)