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 "sparse and noisy lidar completion with rgb guidance and uncertainty"):
  Abstract: This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multitude of applications depend on the awareness of their surroundings, and use depth cues to reason and react accordingly. On the one hand, monocular depth prediction methods fail to generate absolute and precise depth maps. On the other hand, stereoscopic approaches are still significantly outperformed by LiDAR based approaches. The goal of the depth completion task is to generate dense depth predictions from sparse and irregular point clouds which are mapped to a 2D plane. We propose a new framework which extracts both global and local information in order to produce proper depth maps. We argue that simple depth completion does not require a deep network. However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input. This improves the accuracy significantly. Moreover, confidence masks are exploited in order to take into account the uncertainty in the depth predictions from each modality. This fusion method outperforms the state-of-the-art and ranks first on the KITTI depth completion benchmark. Our code with visualizations is available.

Includes members:
  Member "self supervised model adaptation for multimodal semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "confidence propagation through cnns for guided sparse depth regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  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 "self supervised sparse to dense self supervised depth completion from lidar and monocular camera":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  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 "hms net hierarchical multi scale sparsity invariant network for sparse depth completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion")
  Member "in defense of classical image processing fast depth completion on the cpu":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "confidence guided stereo 3d object detection with split depth estimation")
  Member "sparse to dense depth prediction from sparse depth samples and a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")

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)