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 "unsupervised enhancement of real world depth images using tri cycle gan"):
  Abstract: Low quality depth poses a considerable challenge to computer vision algorithms. In this work we aim to enhance highly degraded, real-world depth images acquired by a low-cost sensor, for which an analytical noise model is unavailable. In the absence of clean ground-truth, we approach the task as an unsupervised domain-translation between the low-quality sensor domain and a high-quality sensor domain, represented using two unpaired training sets. We employ the highly-successful Cycle-GAN to this task, but find it to perform poorly in this case. Identifying the sources of the failure, we introduce several modifications to the framework, including a larger generator architecture, depth-specific losses that take into account missing pixels, and a novel Tri-Cycle loss which promotes information-preservation while addressing the asymmetry between the domains. We show that the resulting framework dramatically improves over the original Cycle-GAN both visually and quantitatively, extending its applicability to more challenging and asymmetric translation tasks.

Includes members:
  Member "s flow gan":
    also connected through other hyperedges:
      (none)
  Member "rethinking lossy compression the rate distortion perception tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical quantized autoencoders")
  Member "generative adversarial networks for extreme learned image compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to structure an image with few colors")
  Member "deep depth completion of a single rgb d image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")
  Member "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "medgan medical image translation using gans")
  Member "sparse and dense data with cnns depth completion and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "slimconv reducing channel redundancy in convolutional neural networks by weights flipping")
  Member "depth map completion by jointly exploiting blurry color images and sparse depth maps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predicting unobserved space for planning via depth map augmentation")

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)