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: "attentive generative adversarial network for raindrop removal from a single image"
  Abstract: Raindrops adhered to a glass window or camera lens can severely hamper the visibility of a background scene and degrade an image considerably. In this paper, we address the problem by visually removing raindrops, and thus transforming a raindrop degraded image into a clean one. The problem is intractable, since first the regions occluded by raindrops are not given. Second, the information about the background scene of the occluded regions is completely lost for most part. To resolve the problem, we apply an attentive generative network using adversarial training. Our main idea is to inject visual attention into both the generative and discriminative networks. During the training, our visual attention learns about raindrop regions and their surroundings. Hence, by injecting this information, the generative network will pay more attention to the raindrop regions and the surrounding structures, and the discriminative network will be able to assess the local consistency of the restored regions. This injection of visual attention to both generative and discriminative networks is the main contribution of this paper. Our experiments show the effectiveness of our approach, which outperforms the state of the art methods quantitatively and qualitatively.

Connected through hyperedge HE-1 (induced by source paper "vcnet a robust approach to blind image inpainting"):
  with members:
    - "image inpainting with learnable bidirectional attention maps"
    - "foreground aware image inpainting"
    - "pluralistic image completion"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "a style based generator architecture for generative adversarial networks"
    - "the contextual loss for image transformation with non aligned data"

Connected through hyperedge HE-2 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution"):
  with members:
    - "path restore learning network path selection for image restoration"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "attention based adaptive selection of operations for image restoration in the presence of unknown combined distortions"
    - "mutan multimodal tucker fusion for visual question answering"
    - "single image super resolution via a lightweight residual convolutional neural network"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-3 (induced by source paper "rainy screens collecting rainy datasets indoors"):
  with members:
    - "i can see clearly now image restoration via de raining"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "semi deraingan a new semi supervised single image deraining network"):
  with members:
    - "ladn local adversarial disentangling network for facial makeup and de makeup"
    - "semi supervised transfer learning for image rain removal"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image de raining using a conditional generative adversarial network"
    - "perceptual losses for real time style transfer and super resolution"
    - "deep joint rain detection and removal from a single image"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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-6 (induced by source paper "attention guided generative adversarial networks for unsupervised image to image translation"):
  with members:
    - "attribute guided sketch generation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "attention gan for object transfiguration in wild images"
    - "mask aware photorealistic face attribute manipulation"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-7 (induced by source paper "restoring images with unknown degradation factors by recurrent use of a multi branch network"):
  with members:
    - "dual residual networks leveraging the potential of paired operations for image restoration"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "non locally enhanced encoder decoder network for single image de raining"

Connected through hyperedge HE-8 (induced by source paper "dual residual networks leveraging the potential of paired operations for image restoration"):
  with members:
    - "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"
    - "gated fusion network for single image dehazing"
    - "yolov3 an incremental improvement"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "real world noisy image denoising a new benchmark"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"

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)