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 "an effective two branch model based deep network for single image deraining"):
  Abstract: Removing rain effects from an image automatically has many applications such as autonomous driving, drone piloting and photo editing and still draws the attention of many people. Traditional methods use heuristics to handcraft various priors to remove or separate the rain effects from an image. Recently end-to-end deep learning based deraining methods have been proposed to offer more flexibility and effectiveness. However, they tend not to obtain good visual effect when encountered images with heavy rain. Heavy rain brings not only rain streaks but also haze-like effect which is caused by the accumulation of tiny raindrops. Different from previous deraining methods, in this paper we model rainy images with a new rain model to remove not only rain streaks but also haze-like effect. Guided by our model, we design a two-branch network to learn its parameters. Then, an SPP structure is jointly trained to refine the results of our model to control the degree of removing the haze-like effect flexibly. Besides, a subnetwork which can localize the rainy pixels is proposed to guide the training of our network. Extensive experiments on several datasets show that our method outperforms the state-of-the-art in both objectives assessments and visual quality.

Includes members:
  Member "spatial attentive single image deraining with a high quality real rain dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "single image deraining a comprehensive benchmark analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")
  Member "heavy rain image restoration integrating physics model and conditional adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "physics guided machine learning for scientific discovery an application in simulating lake temperature profiles")
  Member "recurrent squeeze and excitation context aggregation net for single image deraining":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "non locally enhanced encoder decoder network for single image de raining":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "fast single image rain removal via a deep decomposition composition network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gradient information guided deraining with a novel network and adversarial training")
  Member "physics based generative adversarial models for image restoration and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "physics guided machine learning for scientific discovery an application in simulating lake temperature profiles")
  Member "single image deraining using scale aware multi stage recurrent network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ir nas neural architecture search for image restoration")

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)