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: "progressive image deraining networks a better and simpler baseline"
  Abstract: Along with the deraining performance improvement of deep networks, their structures and learning become more and more complicated and diverse, making it difficult to analyze the contribution of various network modules when developing new deraining networks. To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions. Specifically, by repeatedly unfolding a shallow ResNet, progressive ResNet (PRN) is proposed to take advantage of recursive computation. A recurrent layer is further introduced to exploit the dependencies of deep features across stages, forming our progressive recurrent network (PReNet). Furthermore, intra-stage recursive computation of ResNet can be adopted in PRN and PReNet to notably reduce network parameters with graceful degradation in deraining performance. For network input and output, we take both stage-wise result and original rainy image as input to each ResNet and finally output the prediction of {residual image}. As for loss functions, single MSE or negative SSIM losses are sufficient to train PRN and PReNet. Experiments show that PRN and PReNet perform favorably on both synthetic and real rainy images. Considering its simplicity, efficiency and effectiveness, our models are expected to serve as a suitable baseline in future deraining research. The source codes are available at this https URL.

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"
    - "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"
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "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 "nlh a blind pixel level non local method for real world image denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "simultaneous fidelity and regularization learning for image restoration"
    - "toward convolutional blind denoising of real photographs"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "neural nearest neighbors networks"
    - "real world noisy image denoising a new benchmark"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-4 (induced by source paper "deraincyclegan an attention guided unsupervised benchmark for single image deraining and rainmaking"):
  with members:
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "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-5 (induced by source paper "j net randomly weighted u net for audio source separation"):
  with members:
    - "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"
    - "convolutional recurrent neural network based progressive learning for monaural speech enhancement"
    - "audio denoising with deep network priors"
    - "randomly weighted cnns for music audio classification"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "deep image prior"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "convolutional recurrent neural network based progressive learning for monaural speech enhancement"):
  with members:
    - "constrained convolutional recurrent networks to improve speech quality with low impact on recognition accuracy"
    - "convolutional recurrent neural networks for speech enhancement"
    - "supervised speech separation based on deep learning an overview"
    - "a fully convolutional neural network for speech enhancement"
    - "pruning filters for efficient convnets"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deeply recursive convolutional network for image super resolution"
    - "learning deconvolution network for semantic segmentation"

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)