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: "recurrent squeeze and excitation context aggregation net for single image deraining"
  Abstract: Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics. Codes and supplementary material are available at our project webpage: this https URL .

Connected through hyperedge HE-1 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions"):
  with members:
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "i can see clearly now image restoration via de raining"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "bisenet bilateral segmentation network for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "gated fusion network for degraded image super resolution"):
  with members:
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"

Connected through hyperedge HE-3 (induced by source paper "memory efficient hierarchical neural architecture search for image denoising"):
  with members:
    - "generating training data for denoising real rgb images via camera pipeline simulation"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "darts improved differentiable architecture search with early stopping"
    - "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"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "graph hypernetworks for neural architecture search"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deep back projection networks for super resolution"

Connected through hyperedge HE-5 (induced by source paper "deep comprehensive correlation mining for image clustering"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "differentiable linearized admm"
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "essential tensor learning for multi view spectral clustering"
    - "unsupervised representation learning by predicting image rotations"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"

Connected through hyperedge HE-6 (induced by source paper "expectation maximization attention networks for semantic segmentation"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "convolutional neural networks with alternately updated clique"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "a 2 nets double attention networks"
    - "dual attention network for scene segmentation"
    - "context encoding for semantic segmentation"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "ir nas neural architecture search for image restoration"):
  with members:
    - "generating training data for denoising real rgb images via camera pipeline simulation"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "darts improved differentiable architecture search with early stopping"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural nearest neighbors networks"
    - "fast neural architecture search of compact semantic segmentation models via auxiliary cells"

Connected through hyperedge HE-8 (induced by source paper "gradient information guided deraining with a novel network and adversarial training"):
  with members:
    - "rain removal in traffic surveillance does it matter"
    - "learning dual convolutional neural networks for low level vision"
    - "deep ordinal regression network for monocular depth estimation"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "fast single image rain removal via a deep decomposition composition network"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

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)