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: "fast single image rain removal via a deep decomposition composition network"
  Abstract: Rain effect in images typically is annoying for many multimedia and computer vision tasks. For removing rain effect from a single image, deep leaning techniques have been attracting considerable attentions. This paper designs a novel multi-task leaning architecture in an end-to-end manner to reduce the mapping range from input to output and boost the performance. Concretely, a decomposition net is built to split rain images into clean background and rain layers. Different from previous architectures, our model consists of, besides a component representing the desired clean image, an extra component for the rain layer. During the training phase, we further employ a composition structure to reproduce the input by the separated clean image and rain information for improving the quality of decomposition. Experimental results on both synthetic and real images are conducted to reveal the high-quality recovery by our design, and show its superiority over other state-of-the-art methods. Furthermore, our design is also applicable to other layer decomposition tasks like dust removal. More importantly, our method only requires about 50ms, significantly faster than the competitors, to process a testing image in VGA resolution on a GTX 1080 GPU, making it attractive for practical use.

Connected through hyperedge HE-1 (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"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "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"
    - "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"
    - "single image deraining using scale aware multi stage recurrent 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"

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)