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 "ground plane based absolute scale estimation for monocular visual odometry"):
  Abstract: Recovering the absolute metric scale from a monocular camera is a challenging but highly desirable problem for monocular camera-based systems. By using different kinds of cues, various approaches have been proposed for scale estimation, such as camera height, object size etc. In this paper, firstly, we summarize different kinds of scale estimation approaches. Then, we propose a robust divide and conquer the absolute scale estimation method based on the ground plane and camera height by analyzing the advantages and disadvantages of different approaches. By using the estimated scale, an effective scale correction strategy has been proposed to reduce the scale drift during the Monocular Visual Odometry (VO) estimation process. Finally, the effectiveness and robustness of the proposed method have been verified on both public and self-collected image sequences.

Includes members:
  Member "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "ls vo learning dense optical subspace for robust visual odometry estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "learning monocular visual odometry with dense 3d mapping from dense 3d flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation")
  Member "learning based image enhancement for visual odometry in challenging hdr environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "probabilistic global scale estimation for monoslam based on generic object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stereo vision based semantic 3d object and ego motion tracking for autonomous driving")
  Member "a survey of structure from motion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "inertial based scale estimation for structure from motion on mobile devices":
    also connected through other hyperedges:
      (none)
  Member "deepvo a deep learning approach for monocular visual odometry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")

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)