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 "deep single image deraining via estimating transmission and atmospheric light in rainy scenes"):
  Abstract: Rain removal in images/videos is still an important task in computer vision field and attracting attentions of more and more people. Traditional methods always utilize some incomplete priors or filters (e.g. guided filter) to remove rain effect. Deep learning gives more probabilities to better solve this task. However, they remove rain either by evaluating background from rainy image directly or learning a rain residual first then subtracting the residual to obtain a clear background. No other models are used in deep learning based de-raining methods to remove rain and obtain other information about rainy scenes. In this paper, we utilize an extensively-used image degradation model which is derived from atmospheric scattering principles to model the formation of rainy images and try to learn the transmission, atmospheric light in rainy scenes and remove rain further. To reach this goal, we propose a robust evaluation method of global atmospheric light in a rainy scene. Instead of using the estimated atmospheric light directly to learn a network to calculate transmission, we utilize it as ground truth and design a simple but novel triangle-shaped network structure to learn atmospheric light for every rainy image, then fine-tune the network to obtain a better estimation of atmospheric light during the training of transmission network. Furthermore, more efficient ShuffleNet Units are utilized in transmission network to learn transmission map and the de-raining image is then obtained by the image degradation model. By subjective and objective comparisons, our method outperforms the selected state-of-the-art works.

Includes members:
  Member "rain removal in traffic surveillance does it matter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "benchmarking robustness in object detection autonomous driving when winter is coming")
  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 "non locally enhanced encoder decoder network for single image de raining":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "gradient information guided deraining with a novel network and adversarial training")
  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 "deep joint rain detection and removal from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "xception deep learning with depthwise separable convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")

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)