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: "lightweight pyramid networks for image deraining"
  Abstract: Existing deep convolutional neural networks have found major success in image deraining, but at the expense of an enormous number of parameters. This limits their potential application, for example in mobile devices. In this paper, we propose a lightweight pyramid of networks (LPNet) for single image deraining. Instead of designing a complex network structures, we use domain-specific knowledge to simplify the learning process. Specifically, we find that by introducing the mature Gaussian-Laplacian image pyramid decomposition technology to the neural network, the learning problem at each pyramid level is greatly simplified and can be handled by a relatively shallow network with few parameters. We adopt recursive and residual network structures to build the proposed LPNet, which has less than 8K parameters while still achieving state-of-the-art performance on rain removal. We also discuss the potential value of LPNet for other low- and high-level vision tasks.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing mri via a multi scale dilated residual convolution network"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "image super resolution using very deep residual channel attention networks"
    - "a multiscale image denoising algorithm based on dilated residual convolution network"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "depth reconstruction of translucent objects from a single time of flight camera using deep residual networks"
    - "a segmentation aware deep fusion network for compressed sensing mri"
    - "isointense infant brain mri segmentation with a dilated convolutional neural network"
    - "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "progressive image deraining networks a better and simpler baseline"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "simultaneous fidelity and regularization learning for image restoration"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "image de raining using a conditional generative adversarial network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep joint rain detection and removal from a single image"

Connected through hyperedge HE-3 (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"
    - "single image deraining using scale aware multi stage recurrent network"
    - "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-4 (induced by source paper "semi supervised transfer learning for 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"
    - "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"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "image de raining using a conditional generative adversarial network"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "semi supervised cnn for single image rain removal"
    - "residual guide feature fusion network for single image deraining"
    - "rain streak removal for single image via kernel guided cnn"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "density aware single image de raining using a multi stream dense network"

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)