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 selection for least squares pose optimization in vo vslam"
  Abstract: This paper aims to select features that contribute most to the pose estimation in VO/VSLAM. Unlike existing feature selection works that are focused on efficiency only, our method significantly improves the accuracy of pose tracking, while introducing little overhead. By studying the impact of feature selection towards least squares pose optimization, we demonstrate the applicability of improving accuracy via good feature selection. To that end, we introduce the Max-logDet metric to guide the feature selection, which is connected to the conditioning of least squares pose optimization problem. We then describe an efficient algorithm for approximately solving the NP-hard Max-logDet problem. Integrating Max-logDet feature selection into a state-of-the-art visual SLAM system leads to accuracy improvements with low overhead, as demonstrated via evaluation on a public benchmark.

Connected through hyperedge HE-1 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency"):
  with members:
    - "low latency visual slam with appearance enhanced local map building"
    - "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"
    - "low latency visual slam with appearance enhanced local map building"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "low latency visual slam with appearance enhanced local map building"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "appearance based landmark selection for efficient long term visual localization"
    - "ldso direct sparse odometry with loop closure"
    - "experiments in fast autonomous gps denied quadrotor flight"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-4 (induced by source paper "camera pose correction in slam based on bias values of map points"):
  with members:
    - "robocentric visual inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-5 (induced by source paper "resilient sensor architecture design and tradespace analysis for autonomous vehicle localization and mapping"):
  with members:
    - "towards a functional system architecture for automated vehicles"

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)