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: "semantic nearest neighbor fields monocular edge visual odometry"
  Abstract: Recent advances in deep learning for edge detection and segmentation opens up a new path for semantic-edge-based ego-motion estimation. In this work, we propose a robust monocular visual odometry (VO) framework using category-aware semantic edges. It can reconstruct large-scale semantic maps in challenging outdoor environments. The core of our approach is a semantic nearest neighbor field that facilitates a robust data association of edges across frames using semantics. This significantly enlarges the convergence radius during tracking phases. The proposed edge registration method can be easily integrated into direct VO frameworks to estimate photometrically, geometrically, and semantically consistent camera motions. Different types of edges are evaluated and extensive experiments demonstrate that our proposed system outperforms state-of-art indirect, direct, and semantic monocular VO systems.

Connected through hyperedge HE-1 (induced by source paper "robust monocular edge visual odometry through coarse to fine data association"):
  with members:
    - "se slam semi dense structured edge based monocular slam"
    - "ldso direct sparse odometry with loop closure"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "edge slam edge points based monocular visual slam"
    - "semi dense visual odometry for rgb d cameras using approximate nearest neighbour fields"
    - "direct sparse odometry"
    - "holistically nested edge detection"

Connected through hyperedge HE-2 (induced by source paper "robust semi direct monocular visual odometry using edge and illumination robust cost"):
  with members:
    - "loosely coupled semi direct monocular slam"
    - "ldso direct sparse odometry with loop closure"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "edge slam edge points based monocular visual slam"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "holistically nested edge detection"
    - "orb slam a versatile and accurate monocular slam system"

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)