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: "spatial attentive single image deraining with a high quality real rain dataset"
  Abstract: Removing rain streaks from a single image has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining remains an open problem for two reasons. First, existing synthesized rain datasets have only limited realism, in terms of modeling real rain characteristics such as rain shape, direction and intensity. Second, there are no public benchmarks for quantitative comparisons on real rain images, which makes the current evaluation less objective. The core challenge is that real world rain/clean image pairs cannot be captured at the same time. In this paper, we address the single image rain removal problem in two ways. First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images. Using this method, we construct a large-scale dataset of $\sim$$29.5K$ rain/rain-free image pairs that covers a wide range of natural rain scenes. Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner. Extensive experiments demonstrate that our network performs favorably against the state-of-the-art deraining methods.

Connected through hyperedge HE-1 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions"):
  with members:
    - "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"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-2 (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"
    - "progressive image deraining networks a better and simpler baseline"
    - "i can see clearly now image restoration via de raining"
    - "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-3 (induced by source paper "deraincyclegan an attention guided unsupervised benchmark for single image deraining and rainmaking"):
  with members:
    - "progressive image deraining networks a better and simpler baseline"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "semi supervised transfer learning for image rain removal"
    - "density aware single image de raining using a multi stream dense network"
    - "drit diverse image to image translation via disentangled representations"
    - "clearing the skies a deep network architecture for single image rain removal"

Connected through hyperedge HE-4 (induced by source paper "an effective two branch model based deep network for single image deraining"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "fast single image rain removal via a deep decomposition composition network"
    - "physics based generative adversarial models for image restoration and beyond"
    - "single image deraining using scale aware multi stage recurrent network"
    - "clearing the skies a deep network architecture for single image rain removal"

Connected through hyperedge HE-5 (induced by source paper "deep image deraining via intrinsic rainy image priors and multi scale auxiliary decoding"):
  with members:
    - "an effective two branch model based deep network for single image deraining"
    - "progressive image deraining networks a better and simpler baseline"
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "noise2noise learning image restoration without clean data"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "fast single image rain removal via a deep decomposition composition network"
    - "clearing the skies a deep network architecture for single image rain removal"

Connected through hyperedge HE-6 (induced by source paper "gradual network for single image de raining"):
  with members:
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "non local neural networks"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "densely connected convolutional networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep joint rain detection and removal from a single image"
    - "deep residual learning for image recognition"

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)