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: "rain streak removal for single image via kernel guided cnn"
  Abstract: Rain streak removal is an important issue and has recently been investigated extensively. Existing methods, especially the newly emerged deep learning methods, could remove the rain streaks well in many cases. However the essential factor in the generative procedure of the rain streaks, i.e., the motion blur, which leads to the line pattern appearances, were neglected by the deep learning rain streaks approaches and this resulted in over-derain or under-derain results. In this paper, we propose a novel rain streak removal approach using a kernel guided convolutional neural network (KGCNN), achieving the state-of-the-art performance with simple network architectures. We first model the rain streak interference with its motion blur mechanism. Then, our framework starts with learning the motion blur kernel, which is determined by two factors including angle and length, by a plain neural network, denoted as parameter net, from a patch of the texture component. Then, after a dimensionality stretching operation, the learned motion blur kernel is stretched into a degradation map with the same spatial size as the rainy patch. The stretched degradation map together with the texture patch is subsequently input into a derain convolutional network, which is a typical ResNet architecture and trained to output the rain streaks with the guidance of the learned motion blur kernel. Experiments conducted on extensive synthetic and real data demonstrate the effectiveness of the proposed method, which preserves the texture and the contrast while removing the rain streaks.

Connected through hyperedge HE-1 (induced by source paper "joint face completion and super resolution using multi scale feature relation learning"):
  with members:
    - "facial expression restoration based on improved graph convolutional networks"
    - "fcsr gan joint face completion and super resolution via multi task learning"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "geometry aware face completion and editing"
    - "graph neural networks a review of methods and applications"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "generative face completion"

Connected through hyperedge HE-2 (induced by source paper "facial expression restoration based on improved graph convolutional networks"):
  with members:
    - "joint face hallucination and deblurring via structure generation and detail enhancement"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-3 (induced by source paper "unsupervised single image deraining with self supervised constraints"):
  with members:
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "conditional image to image translation"
    - "unsupervised representation learning by predicting image rotations"
    - "density aware single image de raining using a multi stream dense network"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"

Connected through hyperedge HE-4 (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"
    - "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"
    - "lightweight pyramid networks for image deraining"

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)