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 "dfinenet ego motion estimation and depth refinement from sparse noisy depth input with rgb guidance"):
  Abstract: Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution. Accurate depth sensors such as LiDARs are often heavy, expensive and can only provide sparse depth while lighter depth sensors such as stereo cameras are noiser in comparison. We propose an end-to-end learning algorithm that is capable of using sparse, noisy input depth for refinement and depth completion. Our model also produces the camera pose as a byproduct, making it a great solution for autonomous systems. We evaluate our approach on both indoor and outdoor datasets. Empirical results show that our method performs well on the KITTI~\cite{kitti_geiger2012we} dataset when compared to other competing methods, while having superior performance in dealing with sparse, noisy input depth on the TUM~\cite{sturm12iros} dataset.

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 "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 "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scene completenesss aware lidar depth completion for driving scenario")
  Member "real time dense depth estimation by fusing stereo with sparse depth measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d lidar and stereo fusion using stereo matching network with conditional cost volume normalization")
  Member "deep ordinal regression network for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  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 "sfmlearner learning monocular depth ego motion using meaningful geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised deep pose corrections for robust visual odometry")
  Member "sparse and dense data with cnns depth completion and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")

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)