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 "fast underwater image enhancement for improved visual perception"):
  Abstract: In this paper, we present a conditional generative adversarial network-based model for underwater image enhancement in real-time. In order to supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, and local style information. In addition, we present EUVP, a large-scale dataset of a paired and an unpaired collection of underwater images (of poor and good quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. Furthermore, we perform a number of qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance the quality of underwater images from both paired and unpaired training. More importantly, the enhanced images provide improved performances for several standard models for underwater object detection and human pose estimation; hence, the proposed model can be used as an image processing pipeline by visually-guided underwater robots in real-time applications.

Includes members:
  Member "real world underwater enhancement challenging benchmark and efficient solutions":
    also connected through other hyperedges:
      (none)
  Member "real world underwater enhancement challenges benchmarks and solutions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reveal of domain effect how visual restoration contributes to object detection in aquatic scenes")
  Member "watergan unsupervised generative network to enable real time color correction of monocular underwater images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simultaneous enhancement and super resolution of underwater imagery for improved visual perception")
  Member "enhancing underwater imagery using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "simultaneous enhancement and super resolution of underwater imagery for improved visual perception")
  Member "underwater single image color restoration using haze lines and a new quantitative dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "simultaneous enhancement and super resolution of underwater imagery for improved visual perception")
  Member "dslr quality photos on mobile devices with deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "perceptual generative adversarial networks for small object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "extended feature pyramid network for small object detection")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)