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: "a survey on non filter based monocular visual slam systems"
  Abstract: Extensive research in the field of Visual SLAM for the past fifteen years has yielded workable systems that found their way into various applications, such as robotics and augmented reality. Although filter-based (e.g., Kalman Filter, Particle Filter) Visual SLAM systems were common at some time, non-filter based (i.e., akin to SfM solutions), which are more efficient, are becoming the de facto methodology for building a Visual SLAM system. This paper presents a survey that covers the various non-filter based Visual SLAM systems in the literature, detailing the various components of their implementation, while critically assessing the specific strategies made by each proposed system in implementing its components.

Connected through hyperedge HE-1 (induced by source paper "monocular trail detection and tracking aided by visual slam for small unmanned aerial vehicles"):
  with members:
    - "orb slam a versatile and accurate monocular slam system"
    - "difference of normals as a multi scale operator in unorganized point clouds"

Connected through hyperedge HE-2 (induced by source paper "a survey of structure from motion"):
  with members:
    - "shapefit and shapekick for robust scalable structure from motion"
    - "shapefit exact location recovery from corrupted pairwise directions"
    - "exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences"
    - "robust inference for visual inertial sensor fusion"
    - "robust camera location estimation by convex programming"
    - "stable camera motion estimation using convex programming"
    - "exact and stable recovery of rotations for robust synchronization"
    - "eigenvector synchronization graph rigidity and the molecule problem"

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)