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 "uncertainty depth estimation with gated images for 3d reconstruction"):
  Abstract: Gated imaging is an emerging sensor technology for self-driving cars that provides high-contrast images even under adverse weather influence. It has been shown that this technology can even generate high-fidelity dense depth maps with accuracy comparable to scanning LiDAR systems. In this work, we extend the recent Gated2Depth framework with aleatoric uncertainty providing an additional confidence measure for the depth estimates. This confidence can help to filter out uncertain estimations in regions without any illumination. Moreover, we show that training on dense depth maps generated by LiDAR depth completion algorithms can further improve the performance.

Includes members:
  Member "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather":
    also connected through other hyperedges:
      (none)
  Member "sparse and noisy lidar completion with rgb guidance and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "neural rgb d sensing depth and uncertainty from a video camera":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable uncertainty for computer vision with functional variational inference")
  Member "learning single camera depth estimation using dual pixels":
    also connected through other hyperedges:
      (none)
  Member "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "uncertainty estimation in one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesod a bayesian approach for uncertainty estimation in deep object detectors")
  Member "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mvloc multimodal variational geometry aware learning for visual localization")
  Member "gated2depth real time dense lidar from gated images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data")

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)