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: "on manifold preintegration theory for fast and accurate visual inertial navigation"
  Abstract: Current approaches for visual-inertial navigation (VIN) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time; this problem is further emphasized by the fact that inertial measurements come at high rate, hence leading to fast growth of the number of variables in the optimization. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes into single relative motion constraints. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group. We formally discuss the generative measurement model as well as the nature of the rotation noise and derive the expression for the maximum a posteriori state estimator. Our theoretical development enables the computation of all necessary Jacobians for the optimization and a-posteriori bias correction in analytic form. The second contribution is to show that the preintegrated IMU model can be seamlessly integrated into a visual-inertial pipeline under the uni- fying framework of factor graphs. This enables the application of incremental-smoothing algorithms and the use of a structureless model for visual measurements, which avoids optimizing over the 3D points, further accelerating the computation. We perform an extensive evaluation of our monocular VIN pipeline on real and simulated datasets. The results confirm that our modelling effort leads to accurate state estimation in real-time, outperforming state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "kimera an open source library for real time metric semantic localization and mapping"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "dual graph convolutional network for semantic segmentation"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "incremental visual inertial 3d mesh generation with structural regularities"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "active scene understanding via online semantic reconstruction"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"

Connected through hyperedge HE-3 (induced by source paper "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"):
  with members:
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "sim to real learning agile locomotion for quadruped robots"
    - "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-4 (induced by source paper "build your own visual inertial drone a cost effective and open source autonomous drone"):
  with members:
    - "sampling based motion planning for active multirotor system identification"
    - "control of a quadrotor with reinforcement learning"
    - "visual inertial monocular slam with map reuse"
    - "autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle"

Connected through hyperedge HE-5 (induced by source paper "build your own visual inertial odometry aided cost effective and open source autonomous drone"):
  with members:
    - "sampling based motion planning for active multirotor system identification"
    - "control of a quadrotor with reinforcement learning"
    - "visual inertial monocular slam with map reuse"
    - "autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle"

Connected through hyperedge HE-6 (induced by source paper "past present and future of simultaneous localization and mapping toward the robust perception age"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "event based 6 dof camera tracking for high speed applications"
    - "monte carlo localization in hand drawn maps"
    - "monocular slam supported object recognition"
    - "lagrangian duality in 3d slam verification techniques and optimal solutions"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-7 (induced by source paper "attention and anticipation in fast visual inertial navigation"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "you only look once unified real time object detection"
    - "probe predictive robust estimation for visual inertial navigation"
    - "orb slam a versatile and accurate monocular slam system"
    - "high performance and tunable stereo reconstruction"
    - "recurrent models of visual attention"

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)