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 "depth completion from sparse lidar data with depth normal constraints"):
  Abstract: Depth completion aims to recover dense depth maps from sparse depth measurements. It is of increasing importance for autonomous driving and draws increasing attention from the vision community. Most of existing methods directly train a network to learn a mapping from sparse depth inputs to dense depth maps, which has difficulties in utilizing the 3D geometric constraints and handling the practical sensor noises. In this paper, to regularize the depth completion and improve the robustness against noise, we propose a unified CNN framework that 1) models the geometric constraints between depth and surface normal in a diffusion module and 2) predicts the confidence of sparse LiDAR measurements to mitigate the impact of noise. Specifically, our encoder-decoder backbone predicts surface normals, coarse depth and confidence of LiDAR inputs simultaneously, which are subsequently inputted into our diffusion refinement module to obtain the final completion results. Extensive experiments on KITTI depth completion dataset and NYU-Depth-V2 dataset demonstrate that our method achieves state-of-the-art performance. Further ablation study and analysis give more insights into the proposed method and demonstrate the generalization capability and stability of our model.

Includes members:
  Member "confidence propagation through cnns for guided sparse depth regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scene completenesss aware lidar depth completion for driving scenario")
  Member "learning depth with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "deep depth completion of a single rgb d image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")
  Member "depth estimation via affinity learned with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "self supervised sparse to dense self supervised depth completion from lidar and monocular camera":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "propagating confidences through cnns for sparse data regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "depth completion via deep basis fitting")

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)