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 "scene completenesss aware lidar depth completion for driving scenario"):
  Abstract: In this paper we propose Scene Completeness-Aware Depth Completion (SADC) to complete raw lidar scans into dense depth maps with fine whole scene structures. Recent sparse depth completion for lidar only focuses on the lower scenes and produce irregular estimations on the upper because existing datasets such as KITTI do not provide groundtruth for upper areas. These areas are considered less important because they are usually sky or trees and of less scene understanding interest. However, we argue that in several driving scenarios such as large trucks or cars with loads, objects could extend to upper parts of scenes, and thus depth maps with structured upper scene estimation are important for RGBD algorithms. SADC leverages stereo cameras, which have better scene completeness, and lidars, which are more precise, to perform sparse depth completion. To our knowledge, we are the first to focus on scene completeness of sparse depth completion. We validate our SADC on both depth estimate precision and scene-completeness on KITTI. Moreover, SADC only adds small extra computational cost upon base methods of stereo matching and lidar completion in terms of runtime and model size.

Includes members:
  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 "sdnet semantically guided depth estimation network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantically guided representation learning for self supervised monocular depth")
  Member "depth coefficients for depth completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "depth completion from sparse lidar data with depth normal constraints":
    also connected through other hyperedges:
      (none)
  Member "3d lidar and stereo fusion using stereo matching network with conditional cost volume normalization":
    also connected through other hyperedges:
      (none)
  Member "deep rgb d canonical correlation analysis for sparse depth completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection on single monocular images through canonical correlation analysis")
  Member "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "depth completion via deep basis fitting")
  Member "learning depth with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")

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)