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: "good feature matching towards accurate robust vo vslam with low latency"
  Abstract: Analysis of state-of-the-art VO/VSLAM system exposes a gap in balancing performance (accuracy & robustness) and efficiency (latency). Feature-based systems exhibit good performance, yet have higher latency due to explicit data association; direct & semidirect systems have lower latency, but are inapplicable in some target scenarios or exhibit lower accuracy than feature-based ones. This paper aims to fill the performance-efficiency gap with an enhancement applied to feature-based VSLAM. We present good feature matching, an active map-to-frame feature matching method. Feature matching effort is tied to submatrix selection, which has combinatorial time complexity and requires choosing a scoring metric. Via simulation, the Max-logDet matrix revealing metric is shown to perform best. For real-time applicability, the combination of deterministic selection and randomized acceleration is studied. The proposed algorithm is integrated into monocular & stereo feature-based VSLAM systems. Extensive evaluations on multiple benchmarks and compute hardware quantify the latency reduction and the accuracy & robustness preservation.

Connected through hyperedge HE-1 (induced by source paper "closed loop benchmarking of stereo visual inertial slam systems understanding the impact of drift and latency on tracking accuracy"):
  with members:
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "characterizing slam benchmarks and methods for the robust perception age"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "robust stereo visual inertial odometry for fast autonomous flight"

Connected through hyperedge HE-2 (induced by source paper "characterizing slam benchmarks and methods for the robust perception age"):
  with members:
    - "low latency visual slam with appearance enhanced local map building"
    - "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)