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 "deep direct visual odometry"):
  Abstract: Monocular direct visual odometry (DVO) relies heavily on high-quality images and good initial pose estimation for accuracy tracking process, which means that DVO may fail if the image quality is poor or the initial value is incorrect. In this study, we present a new architecture to overcome the above limitations by embedding deep learning into DVO. A novel self-supervised network architecture for effectively predicting 6-DOF pose is proposed in this paper, and we incorporate the pose prediction into Direct Sparse Odometry (DSO) for robust initialization and tracking process. Furthermore, the attention mechanism is included to select useful features for accurate pose regression. The experiments on the KITTI dataset show that the proposed network achieves an outstanding performance compared with previous self-supervised methods, and the integration with pose network makes the initialization and tracking of DSO more robust and accurate.

Includes members:
  Member "selective sensor fusion for neural visual inertial odometry":
    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 "stgan a unified selective transfer network for arbitrary image attribute editing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "a unified framework for mutual improvement of slam and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "movable object aware visual slam via weakly supervised semantic segmentation")
  Member "dsvo direct stereo visual odometry":
    also connected through other hyperedges:
      (none)
  Member "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints":
    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 "cnn svo improving the mapping in semi direct visual odometry using single image depth prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visual odometry revisited what should be learnt")
  Member "df net unsupervised joint learning of depth and flow using cross task consistency":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "direct sparse visual inertial odometry using dynamic marginalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)