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 "image guided depth sampling and reconstruction"):
  Abstract: Depth acquisition, based on active illumination, is essential for autonomous and robotic navigation. LiDARs (Light Detection And Ranging) with mechanical, fixed, sampling templates are commonly used in today's autonomous vehicles. An emerging technology, based on solid-state depth sensors, with no mechanical parts, allows fast, adaptive, programmable scans. #R##N#In this paper, we investigate the topic of adaptive, image-driven, sampling and reconstruction strategies. First, we formulate a piece-wise linear depth model with several tolerance parameters and estimate its validity for indoor and outdoor scenes. Our model and experiments predict that, in the optimal case, about 20-60 piece-wise linear structures can approximate well a depth map. This translates to a depth-to-image sampling ratio of about 1/1200. We propose a simple, generic, sampling and reconstruction algorithm, based on super-pixels. We reach a sampling rate which is still far from the optimal case. However, our sampling improves grid and random sampling, consistently, for a wide variety of reconstruction methods. Moreover, our proposed reconstruction achieves state-of-the-art results, compared to image-guided depth completion algorithms, reducing the required sampling rate by a factor of 3-4. A single-pixel depth camera built in our lab illustrates the concept.

Includes members:
  Member "self supervised sparse to dense self supervised depth completion from lidar and monocular camera":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "depth completion via deep basis fitting")
  Member "hms net hierarchical multi scale sparsity invariant network for sparse depth completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion")
  Member "estimating depth from rgb and sparse sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "in defense of classical image processing fast depth completion on the cpu":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "confidence guided stereo 3d object detection with split depth estimation")
  Member "deep convolutional compressed sensing for lidar depth completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scene completenesss aware lidar depth completion for driving scenario")
  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)