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: "on the duality between retinex and image dehazing"
  Abstract: Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.

Connected through hyperedge HE-1 (induced by source paper "underexposed image correction via hybrid priors navigated deep propagation"):
  with members:
    - "kindling the darkness a practical low light image enhancer"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "yolov3 an incremental improvement"
    - "deep retinex decomposition for low light enhancement"
    - "residual dense network for image super resolution"
    - "learning collaborative generation correction modules for blind image deblurring and beyond"
    - "deep image prior"

Connected through hyperedge HE-2 (induced by source paper "led2net deep illumination aware dehazing with low light and detail enhancement"):
  with members:
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "gated fusion network for single image dehazing"
    - "densely connected pyramid dehazing network"
    - "msr net low light image enhancement using deep convolutional network"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "dehazenet an end to end system for single image haze removal"
    - "deep outdoor illumination estimation"
    - "llnet a deep autoencoder approach to natural low light image enhancement"

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)