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: "kindling the darkness a practical low light image enhancer"
  Abstract: Images captured under low-light conditions often suffer from (partially) poor visibility. Besides unsatisfactory lightings, multiple types of degradations, such as noise and color distortion due to the limited quality of cameras, hide in the dark. In other words, solely turning up the brightness of dark regions will inevitably amplify hidden artifacts. This work builds a simple yet effective network for \textbf{Kin}dling the \textbf{D}arkness (denoted as KinD), which, inspired by Retinex theory, decomposes images into two components. One component (illumination) is responsible for light adjustment, while the other (reflectance) for degradation removal. In such a way, the original space is decoupled into two smaller subspaces, expecting to be better regularized/learned. It is worth to note that our network is trained with paired images shot under different exposure conditions, instead of using any ground-truth reflectance and illumination information. Extensive experiments are conducted to demonstrate the efficacy of our design and its superiority over state-of-the-art alternatives. Our KinD is robust against severe visual defects, and user-friendly to arbitrarily adjust light levels. In addition, our model spends less than 50ms to process an image in VGA resolution on a 2080Ti GPU. All the above merits make our KinD attractive for practical use.

Connected through hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "residual non local attention networks for image restoration"
    - "making convolutional networks shift invariant again"
    - "a deep journey into super resolution a survey"
    - "selective kernel networks"
    - "deep learning for image super resolution a survey"
    - "real image denoising with feature attention"
    - "deep high resolution representation learning for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "self supervised image enhancement network training with low light images only"):
  with members:
    - "feedback network for image super resolution"
    - "yolov3 an incremental improvement"
    - "toward convolutional blind denoising of real photographs"
    - "deep retinex decomposition for low light enhancement"
    - "learning to see in the dark"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "underexposed image correction via hybrid priors navigated deep propagation"):
  with members:
    - "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"
    - "deep bilateral learning for real time image enhancement"

Connected through hyperedge HE-4 (induced by source paper "enhancing underexposed photos using perceptually bidirectional similarity"):
  with members:
    - "enlightengan deep light enhancement without paired supervision"
    - "deep retinex decomposition for low light enhancement"
    - "image correction via deep reciprocating hdr transformation"
    - "learning to see in the dark"
    - "deep bilateral learning for real time image enhancement"
    - "llnet a deep autoencoder approach to natural low light image enhancement"
    - "automatic photo adjustment using deep neural networks"

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)