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 "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.

Includes members:
  Member "low latency visual slam with appearance enhanced local map building":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "characterizing slam benchmarks and methods for the robust perception age")
  Member "good feature selection for least squares pose optimization in vo vslam":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "characterizing slam benchmarks and methods for the robust perception age")
  Member "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "closed loop benchmarking of stereo visual inertial slam systems understanding the impact of drift and latency on tracking accuracy")
  Member "the open vision computer an integrated sensing and compute system for mobile robots":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mavnet an effective semantic segmentation micro network for mav based tasks")
  Member "direct sparse odometry with rolling shutter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rolling shutter modelling for direct visual inertial odometry")
  Member "futuremapping the computational structure of spatial ai systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bundle adjustment on a graph processor")
  Member "vins mono a robust and versatile monocular visual inertial state estimator":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "the tum vi benchmark for evaluating visual inertial odometry":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation")

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.RO(Robotics)