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: "non locally enhanced encoder decoder network for single image de raining"
  Abstract: Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks. However, existing deep learning based methods either focus on the entrance and exit of the network by decomposing the input image into high and low frequency information and employing residual learning to reduce the mapping range, or focus on the introduction of cascaded learning scheme to decompose the task of rain streaks removal into multi-stages. These methods treat the convolutional neural network as an encapsulated end-to-end mapping module without deepening into the rationality and superiority of neural network design. In this paper, we delve into an effective end-to-end neural network structure for stronger feature expression and spatial correlation learning. Specifically, we propose a non-locally enhanced encoder-decoder network framework, which consists of a pooling indices embedded encoder-decoder network to efficiently learn increasingly abstract feature representation for more accurate rain streaks modeling while perfectly preserving the image detail. The proposed encoder-decoder framework is composed of a series of non-locally enhanced dense blocks that are designed to not only fully exploit hierarchical features from all the convolutional layers but also well capture the long-distance dependencies and structural information. Extensive experiments on synthetic and real datasets demonstrate that the proposed method can effectively remove rain-streaks on rainy image of various densities while well preserving the image details, which achieves significant improvements over the recent state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "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-2 (induced by source paper "depthwise non local module for fast salient object detection using a single thread"):
  with members:
    - "motion guided attention for video salient object detection"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "a simple pooling based design for real time salient object detection"
    - "non local context encoder robust biomedical image segmentation against adversarial attacks"
    - "rosa robust salient object detection against adversarial attacks"
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-3 (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"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

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"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "real world noisy image denoising a new benchmark"
    - "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 "combinational class activation maps for weakly supervised object localization"):
  with members:
    - "residual non local attention networks for image restoration"
    - "semantic matching by weakly supervised 2d point set registration"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "long term feature banks for detailed video understanding"
    - "non local netvlad encoding for video classification"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-6 (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"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "graph representation learning via hard and channel wise attention networks"):
  with members:
    - "st unet a spatio temporal u network for graph structured time series modeling"
    - "learning visual question answering by bootstrapping hard attention"
    - "hierarchical graph representation learning with differentiable pooling"
    - "large scale learnable graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "non local neural networks"
    - "attention is all you need"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "spatial attentive single image deraining with a high quality real rain dataset"):
  with members:
    - "residual guide feature fusion network for single image deraining"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "robust video content alignment and compensation for rain removal in a cnn framework"
    - "density aware single image de raining using a multi stream dense network"
    - "renoir a dataset for real low light image noise reduction"
    - "vital visual tracking via adversarial learning"
    - "what can help pedestrian detection"
    - "direction aware spatial context features for shadow detection"

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)