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: "i can see clearly now image restoration via de raining"
  Abstract: We present a method for improving segmentation tasks on images affected by adherent rain drops and streaks. We introduce a novel stereo dataset recorded using a system that allows one lens to be affected by real water droplets while keeping the other lens clear. We train a denoising generator using this dataset and show that it is effective at removing the effect of real water droplets, in the context of image reconstruction and road marking segmentation. To further test our de-noising approach, we describe a method of adding computer-generated adherent water droplets and streaks to any images, and use this technique as a proxy to demonstrate the effectiveness of our model in the context of general semantic segmentation. We benchmark our results using the CamVid road marking segmentation dataset, Cityscapes semantic segmentation datasets and our own real-rain dataset, and show significant improvement on all tasks.

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"
    - "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"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-2 (induced by source paper "rainy screens collecting rainy datasets indoors"):
  with members:
    - "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"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-3 (induced by source paper "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"):
  with members:
    - "semantic segmentation of panoramic images using a synthetic dataset"
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "don t worry about the weather unsupervised condition dependent domain adaptation"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"
    - "a cross season correspondence dataset for robust semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "domain bridge for unpaired image to image translation and unsupervised domain adaptation"):
  with members:
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "towards instance level image to image translation"
    - "physics based rendering for improving robustness to rain"
    - "few shot unsupervised image to image translation"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation"
    - "label propagation for deep semi supervised learning"
    - "learning across tasks and domains"

Connected through hyperedge HE-5 (induced by source paper "let s get dirty gan based data augmentation for soiling and adverse weather classification in autonomous driving"):
  with members:
    - "fisheyemultinet real time multi task learning architecture for surround view automated parking system"
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "single image deraining a comprehensive benchmark analysis"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-6 (induced by source paper "segment for restoration restore for segmentation"):
  with members:
    - "maskgan towards diverse and interactive facial image manipulation"
    - "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cycle consistent conditional adversarial transfer networks"
    - "guided image to image translation with bi directional feature transformation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-7 (induced by source paper "robust semantic segmentation in adverse weather conditions by means of sensor data fusion"):
  with members:
    - "semantic segmentation of video sequences with convolutional lstms"
    - "semantic foggy scene understanding with synthetic data"
    - "optimal sensor data fusion architecture for object detection in adverse weather conditions"
    - "boosting lidar based semantic labeling by cross modal training data generation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "deep semantic classification for 3d lidar data"
    - "fast lidar based road detection using fully convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "soilingnet soiling detection on automotive surround view cameras"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "semantic foggy scene understanding with synthetic data"
    - "multimodal unsupervised image to image translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)