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: "msr net low light image enhancement using deep convolutional network"
  Abstract: Images captured in low-light conditions usually suffer from very low contrast, which increases the difficulty of subsequent computer vision tasks in a great extent. In this paper, a low-light image enhancement model based on convolutional neural network and Retinex theory is proposed. Firstly, we show that multi-scale Retinex is equivalent to a feedforward convolutional neural network with different Gaussian convolution kernels. Motivated by this fact, we consider a Convolutional Neural Network(MSR-net) that directly learns an end-to-end mapping between dark and bright images. Different fundamentally from existing approaches, low-light image enhancement in this paper is regarded as a machine learning problem. In this model, most of the parameters are optimized by back-propagation, while the parameters of traditional models depend on the artificial setting. Experiments on a number of challenging images reveal the advantages of our method in comparison with other state-of-the-art methods from the qualitative and quantitative perspective.

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"
    - "kindling the darkness a practical low light image enhancer"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "low light image enhancement algorithm based on retinex and generative adversarial network"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "features for multi target multi camera tracking and re identification"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "a twofold siamese network for real time object tracking"
    - "yolov3 an incremental improvement"
    - "deep retinex decomposition for low light enhancement"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "learning to see in the dark"

Connected through hyperedge HE-3 (induced by source paper "kindling the darkness a practical low light image enhancer"):
  with members:
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "deep retinex decomposition for low light enhancement"
    - "learning to see in the dark"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a bio inspired multi exposure fusion framework for low light image enhancement"
    - "dehazenet an end to end system for single image haze removal"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (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"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "on the duality between retinex and image dehazing"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"):
  with members:
    - "yolov3 an incremental improvement"
    - "mv yolo motion vector aided tracking by semantic object detection"
    - "focal loss for dense object detection"
    - "deep bilateral learning for real time image enhancement"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "dehazenet an end to end system for single image haze removal"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "fused dnn a deep neural network fusion approach to fast and robust pedestrian detection"

Connected through hyperedge HE-6 (induced by source paper "image correction via deep reciprocating hdr transformation"):
  with members:
    - "stylizing face images via multiple exemplars"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "learning high dynamic range from outdoor panoramas"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "learning deep cnn denoiser prior for image restoration"
    - "segflow joint learning for video object segmentation and optical flow"
    - "memnet a persistent memory network for image restoration"
    - "crest convolutional residual learning for visual tracking"

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)