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: "does haze removal help cnn based image classification"
  Abstract: Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "classification driven single image dehazing"):
  with members:
    - "gated fusion network for single image dehazing"
    - "densely connected pyramid dehazing network"
    - "squeeze and excitation networks"
    - "image de raining using a conditional generative adversarial network"
    - "residual attention network for image classification"
    - "perceptual losses for real time style transfer and super resolution"
    - "dehazenet an end to end system for single image haze removal"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "real world underwater enhancement challenging benchmark and efficient solutions"):
  with members:
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "yolov3 an incremental improvement"
    - "densely connected pyramid dehazing network"
    - "towards quality advancement of underwater machine vision with generative adversarial networks"
    - "learning aggregated transmission propagation networks for haze removal and beyond"
    - "dehazenet an end to end system for single image haze removal"
    - "deep residual learning for image recognition"
    - "deepfont identify your font from an image"

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)