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 "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"):
  Abstract: Recently end-to-end unsupervised deep learning methods have demonstrated an impressive performance for visual depth and ego-motion estimation tasks. These data-based learning methods do not rely on the same limiting assumptions that geometry-based methods do. The encoder–decoder network has been widely used in the depth estimation and the RCNN has brought significant improvements in the ego-motion estimation. Furthermore, the latest use of generative adversarial nets (GANs) in depth and ego-motion estimation has demonstrated that the estimation could be further improved by generating pictures in the game learning process. This paper proposes a novel unsupervised network system for visual depth and ego-motion estimation—stacked generative adversarial network. It consists of a stack of GAN layers, of which the lowest layer estimates the depth and ego-motion while the higher layers estimate the spatial features. It can also capture the temporal dynamic due to the use of a recurrent representation across the layers. We select the most commonly used KITTI data set for evaluation. The evaluation results show that our proposed method can produce better or comparable results in depth and ego-motion estimation.

Includes members:
  Member "unsupervised adversarial depth estimation using cycled generative networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "progressive fusion for unsupervised binocular depth estimation using cycled networks")
  Member "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "joint unsupervised learning of optical flow and depth by watching stereo videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bridging stereo matching and optical flow via spatiotemporal correspondence")
  Member "single view stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multimodal end to end autonomous driving")
  Member "geonet unsupervised learning of dense depth optical flow and camera pose":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "superdepth self supervised super resolved monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised learning of depth optical flow and pose with occlusion from 3d geometry")
  Member "digging into self supervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")

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.RO(Robotics)