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: "joint rain detection and removal via iterative region dependent multi task learning"
  Abstract: In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain accumulation. Our core ideas lie in our new rain image models and a novel deep learning architecture. We first modify the commonly used model, which is a linear combination of a rain streak layer and a background layer, by adding a binary map that locates rain streak regions. Second, we create a model consisting of a component representing rain accumulation (where individual streaks cannot be seen, and thus visually similar to mist or fog), and another component representing various shapes and directions of overlapping rain streaks, which normally happen in heavy rain. Based on the first model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output. The additional binary map is critically beneficial, since its loss function can provide additional strong information to the network. In many cases though, rain streaks can be dense and large in their size, thus to obtain the clean background, we need spatial contextual information. For this, we utilize the dilated convolution. To handle rain accumulation (again, a phenomenon visually similar to mist or fog) and various shapes and directions of overlapping rain streaks, we propose an iterative information feedback (IIF) network that removes rain streaks and clears up the rain accumulation iteratively and progressively. Overall, this multi-task learning and iterative information feedback benefits each other and constitutes a network that is end-to-end trainable. Our extensive evaluation on real images, particularly on heavy rain, shows the effectiveness of our novel models and architecture, outperforming the state-of-the-art methods significantly.

Connected through hyperedge HE-1 (induced by source paper "fast underwater image enhancement for improved visual perception"):
  with members:
    - "real world underwater enhancement challenging benchmark and efficient solutions"
    - "real world underwater enhancement challenges benchmarks and solutions"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "enhancing underwater imagery using generative adversarial networks"
    - "underwater single image color restoration using haze lines and a new quantitative dataset"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "perceptual generative adversarial networks for small object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "heavy rain image restoration integrating physics model and conditional adversarial learning"):
  with members:
    - "robust video content alignment and compensation for rain removal in a cnn framework"
    - "fast end to end trainable guided filter"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "playing for benchmarks"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"
    - "dehazenet an end to end system for single image haze removal"

Connected through hyperedge HE-3 (induced by source paper "fast perceptual image enhancement"):
  with members:
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "wespe weakly supervised photo enhancer for digital cameras"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "recurrent squeeze and excitation context aggregation net for single image deraining"):
  with members:
    - "single image deraining using scale aware multi stage recurrent network"
    - "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"
    - "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"

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"

Connected through hyperedge HE-6 (induced by source paper "dslr quality photos on mobile devices with deep convolutional networks"):
  with members:
    - "wespe weakly supervised photo enhancer for digital cameras"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "perceptual losses for real time style transfer and super resolution"
    - "dehazenet an end to end system for single image haze removal"
    - "compression artifacts removal using convolutional neural networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "robust optical flow estimation in rainy scenes"):
  with members:
    - "playing for benchmarks"
    - "dehazenet an end to end system for single image haze removal"
    - "optical flow estimation using a spatial pyramid 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)