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: "single image deraining using scale aware multi stage recurrent network"
  Abstract: Given a single input rainy image, our goal is to visually remove rain streaks and the veiling effect caused by scattering and transmission of rain streaks and rain droplets. We are particularly concerned with heavy rain, where rain streaks of various sizes and directions can overlap each other and the veiling effect reduces contrast severely. To achieve our goal, we introduce a scale-aware multi-stage convolutional neural network. Our main idea here is that different sizes of rain-streaks visually degrade the scene in different ways. Large nearby streaks obstruct larger regions and are likely to reflect specular highlights more prominently than smaller distant streaks. These different effects of different streaks have their own characteristics in their image features, and thus need to be treated differently. To realize this, we create parallel sub-networks that are trained and made aware of these different scales of rain streaks. To our knowledge, this idea of parallel sub-networks that treats the same class of objects according to their unique sub-classes is novel, particularly in the context of rain removal. To verify our idea, we conducted experiments on both synthetic and real images, and found that our method is effective and outperforms the state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "an effective two branch model based deep network for single image deraining"):
  with members:
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "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"
    - "clearing the skies a deep network architecture for single image rain removal"

Connected through hyperedge HE-4 (induced by source paper "deep single image deraining via estimating transmission and atmospheric light in rainy scenes"):
  with members:
    - "rain removal in traffic surveillance does it matter"
    - "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"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "deep joint rain detection and removal from a single image"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "semi supervised cnn for single image rain removal"):
  with members:
    - "a deep tree structured fusion model for single image deraining"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "density aware single image de raining using a multi stream dense network"
    - "lightweight pyramid networks for image deraining"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "image de raining using a conditional generative adversarial network"
    - "deep joint rain detection and removal from a single image"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "recurrent squeeze and excitation context aggregation net for single image deraining"):
  with members:
    - "clearing the skies a deep network architecture for single image rain removal"
    - "squeeze and excitation networks"
    - "image de raining using a conditional generative adversarial network"
    - "fast image processing with fully convolutional networks"
    - "joint rain detection and removal via iterative region dependent multi task learning"
    - "deep joint rain detection and removal from a single image"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)