human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "semi supervised cnn for single image rain removal"):
  Abstract: Single image rain removal is a typical inverse problem in computer vision. The deep learning technique has been verified to be effective to this task and achieved state-of-the-art performance. However, the method needs to pre-collect a large set of image pairs with/without rains for training, which not only makes the method laborsome to be practically implemented, but also tends to make the trained network bias to the training samples while less generalized to test samples with unseen rain types in training. To this issue, this paper firstly proposes a semisupervised learning paradigm to this task. Different from traditional deep learning methods which use only supervised image pairs with/without rains, we put the real rainy images, without need of their clean ones, into the network training process as well. This is realized by elaborately formulating the residual between an input rainy image and its expected network output (clear image without rains) as a concise patch-wised Mixture of Gaussians distribution. The entire objective function for training network is thus the combination of the supervised data loss (least square loss between input clear image and the network output) and the unsupervised data loss. In this way, all such unsupervised rainy images, which is much easier to collect than supervised ones, can be rationally fed into the network training process, and thus both the short-of-training-sample and bias-to-supervised-sample issues can be evidently alleviated. Experiments implemented on synthetic and real data experiments verify the superiority of our model as compared to the state-of-the-arts.

Includes members:
  Member "a deep tree structured fusion model for single image deraining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised transfer learning for image rain removal")
  Member "recurrent squeeze and excitation context aggregation net for single image deraining":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "density aware single image de raining using a multi stream dense network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "lightweight pyramid networks for image deraining":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed sensing mri via a multi scale dilated residual convolution network")
  Member "single image deraining using scale aware multi stage recurrent network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ir nas neural architecture search for image restoration")
  Member "clearing the skies a deep network architecture for single image rain removal":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")
  Member "image de raining using a conditional generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medgan medical image translation using gans")
  Member "deep joint rain detection and removal from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gated fusion network for degraded image super resolution")

Question: Please tell me which class the source paper 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)