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: "vins mono a robust and versatile monocular visual inertial state estimator"
  Abstract: One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-inertial system (VINS), which is the minimum sensor suite (in size, weight, and power) for the metric six degrees-of-freedom (DOF) state estimation. In this paper, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator. Our approach starts with a robust procedure for estimator initialization. A tightly coupled, nonlinear optimization-based method is used to obtain highly accurate visual-inertial odometry by fusing preintegrated IMU measurements and feature observations. A loop detection module, in combination with our tightly coupled formulation, enables relocalization with minimum computation. We additionally perform 4-DOF pose graph optimization to enforce the global consistency. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. The current and previous maps can be merged together by the global pose graph optimization. We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms. We also perform an onboard closed-loop autonomous flight on the microaerial-vehicle platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy in localization. We open source our implementations for both PCs ( https://github.com/HKUST-Aerial-Robotics/VINS-Mono ) and iOS mobile devices (  https://github.com/HKUST-Aerial-Robotics/VINS-Mobile ).

Connected through hyperedge HE-1 (induced by source paper "deep drone racing from simulation to reality with domain randomization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep drone racing learning agile flight in dynamic environments"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "autonomous drone race a computationally efficient vision based navigation and control strategy"
    - "beauty and the beast optimal methods meet learning for drone racing"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "teaching uavs to race end to end regression of agile controls in simulation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"

Connected through hyperedge HE-3 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation"):
  with members:
    - "visual inertial odometry of aerial robots"
    - "ronin robust neural inertial navigation in the wild benchmark evaluations and new methods"
    - "rins w robust inertial navigation system on wheels"
    - "ai imu dead reckoning"
    - "selfvio self supervised deep monocular visual inertial odometry and depth estimation"
    - "a mathematical framework for imu error propagation with applications to preintegration"
    - "using inertial sensors for position and orientation estimation"
    - "ionet learning to cure the curse of drift in inertial odometry"

Connected through hyperedge HE-4 (induced by source paper "plug and play slam a unified slam architecture for modularity and ease of use"):
  with members:
    - "least squares optimization from theory to practice"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "proslam graph slam from a programmer s perspective"
    - "hbst a hamming distance embedding binary search tree for feature based visual place recognition"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "keyframe based monocular slam"
    - "visual inertial monocular slam with map reuse"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-5 (induced by source paper "globally optimal consensus maximization for robust visual inertial localization in point and line map"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "2 entity ransac for robust visual localization in changing environment"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "laser map aided visual inertial localization in changing environment"
    - "a minimal closed form solution for multi perspective pose estimation using points and lines"
    - "visual inertial monocular slam with map reuse"
    - "globally optimal inlier set maximisation for simultaneous camera pose and feature correspondence"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-6 (induced by source paper "alternating minimization based trajectory generation for quadrotor aggressive flight"):
  with members:
    - "detailed proofs of alternating minimization based trajectory generation for quadrotor aggressive flight"
    - "teach repeat replan a complete and robust system for aggressive flight in complex environments"
    - "fast uav trajectory optimization using bilevel optimization with analytical gradients"
    - "a hybrid method for online trajectory planning of mobile robots in cluttered environments"
    - "computing real roots of real polynomials"
    - "sampling based algorithms for optimal motion planning"
    - "univariate polynomial real root isolation continued fractions revisited"

Connected through hyperedge HE-7 (induced by source paper "redesigning slam for arbitrary multi camera systems"):
  with members:
    - "voxel map for visual slam"
    - "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-8 (induced by source paper "uwb based system for uav localization in gnss denied environments characterization and dataset"):
  with members:
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "accurate 3d localization for mav swarms by uwb and imu fusion"
    - "robust target relative localization with ultra wideband ranging and communication"
    - "uwb lidar fusion for cooperative range only slam"
    - "an integrated localization navigation scheme for distance based docking of uavs"
    - "ultra wideband aided fast localization and mapping system"
    - "cooperative relative positioning of mobile users by fusing imu inertial and uwb ranging information"

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)