human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "cnn svo improving the mapping in semi direct visual odometry using single image depth prediction"
  Abstract: Reliable feature correspondence between frames is a critical step in visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) algorithms. In comparison with existing VO and V-SLAM algorithms, semi-direct visual odometry (SVO) has two main advantages that lead to state-of-the-art frame rate camera motion estimation: direct pixel correspondence and efficient implementation of probabilistic mapping method. This paper improves the SVO mapping by initializing the mean and the variance of the depth at a feature location according to the depth prediction from a single-image depth prediction network. By significantly reducing the depth uncertainty of the initialized map point (i.e., small variance centred about the depth prediction), the benefits are twofold: reliable feature correspondence between views and fast convergence to the true depth in order to create new map points. We evaluate our method with two outdoor datasets: KITTI dataset and Oxford Robotcar dataset. The experimental results indicate that the improved SVO mapping results in increased robustness and camera tracking accuracy.

Connected through hyperedge HE-1 (induced by source paper "visual odometry revisited what should be learnt"):
  with members:
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "self supervised learning for single view depth and surface normal estimation"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "pose graph optimization for unsupervised monocular visual odometry"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-2 (induced by source paper "deep direct visual odometry"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "a unified framework for mutual improvement of slam and semantic segmentation"
    - "dsvo direct stereo visual odometry"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-3 (induced by source paper "distancenet estimating traveled distance from monocular images using a recurrent convolutional neural network"):
  with members:
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "magicvo end to end monocular visual odometry through deep bi directional recurrent convolutional neural network"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "guided feature selection for deep visual odometry"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "semi supervised monocular depth estimation with left right consistency using deep neural network"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "single view stereo matching"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "sparsity invariant cnns"
    - "semi supervised deep learning for monocular depth map prediction"
    - "cnn slam real time dense monocular slam with learned depth prediction"

Question: Please tell me which class the center node 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)