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: "low latency visual slam with appearance enhanced local map building"
  Abstract: A local map module is often implemented in modern VO/VSLAM systems to improve data association and pose estimation. Conventionally, the local map contents are determined by co-visibility. While co-visibility is cheap to establish, it utilizes the relatively-weak temporal prior (i.e. seen before, likely to be seen now), therefore admitting more features into the local map than necessary. This paper describes an enhancement to co-visibility local map building by incorporating a strong appearance prior, which leads to a more compact local map and latency reduction in downstream data association. The appearance prior collected from the current image influences the local map contents: only the map features visually similar to the current measurements are potentially useful for data association. To that end, mapped features are indexed and queried with Multi-index Hashing (MIH). An online hash table selection algorithm is developed to further reduce the query overhead of MIH and the local map size. The proposed appearance-based local map building method is integrated into a state-of-the-art VO/VSLAM system. When evaluated on two public benchmarks, the size of the local map, as well as the latency of real-time pose tracking in VO/VSLAM are significantly reduced. Meanwhile, the VO/VSLAM mean performance is preserved or improves.

Connected through hyperedge HE-1 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency"):
  with members:
    - "good feature selection for least squares pose optimization in vo vslam"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "the open vision computer an integrated sensing and compute system for mobile robots"
    - "direct sparse odometry with rolling shutter"
    - "futuremapping the computational structure of spatial ai systems"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"

Connected through hyperedge HE-2 (induced by source paper "characterizing slam benchmarks and methods for the robust perception age"):
  with members:
    - "good feature matching towards accurate robust vo vslam with low latency"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "a photometrically calibrated benchmark for monocular visual odometry"

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.RO(Robotics)