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: "when image denoising meets high level vision tasks a deep learning approach"
  Abstract: Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them. First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We demonstrate that on one hand, the proposed denoiser has the generality to overcome the performance degradation of different high-level vision tasks. On the other hand, with the guidance of high-level vision information, the denoising network can generate more visually appealing results. To the best of our knowledge, this is the first work investigating the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning. The code is available online this https URL.

Connected through hyperedge HE-1 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning"):
  with members:
    - "deceiving image to image translation networks for autonomous driving with adversarial perturbations"
    - "events to video bringing modern computer vision to event cameras"
    - "end to end learning of representations for asynchronous event based data"
    - "a deep journey into super resolution a survey"
    - "focus is all you need loss functions for event based vision"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "the relativistic discriminator a key element missing from standard gan"

Connected through hyperedge HE-2 (induced by source paper "improving robustness of deep learning based image reconstruction"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "gan based projector for faster recovery with convergence guarantees in linear inverse problems"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "scaling provable adversarial defenses"
    - "adversarially robust generalization requires more data"
    - "generating adversarial examples with adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "joint enhancement and denoising method via sequential decomposition"
    - "getting to know low light images with the exclusively dark dataset"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-4 (induced by source paper "segmentation aware image denoising without knowing true segmentation"):
  with members:
    - "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"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "yolov3 an incremental improvement"
    - "toward convolutional blind denoising of real photographs"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "connecting image denoising and high level vision tasks via deep learning"

Connected through hyperedge HE-5 (induced by source paper "deblurgan v2 deblurring orders of magnitude faster and better"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "gated fusion network for joint image deblurring and super resolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the relativistic discriminator a key element missing from standard gan"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-6 (induced by source paper "collaborative global local networks for memory efficient segmentation of ultra high resolution images"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "connecting image denoising and high level vision tasks via deep learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (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"
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "blind universal bayesian image denoising with gaussian noise level learning"):
  with members:
    - "class aware fully convolutional gaussian and poisson denoising"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised discovery of object landmarks as structural representations"
    - "deep learning for image denoising a survey"
    - "unprocessing images for learned raw denoising"
    - "learning a single convolutional super resolution network for multiple degradations"

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)