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: "laser map aided visual inertial localization in changing environment"
  Abstract: Long-term visual localization in outdoor environment is a challenging problem, especially faced with the cross-seasonal, bi-directional tasks and changing environment. In this paper we propose a novel visual inertial localization framework that localizes against the LiDAR-built map. Based on the geometry information of the laser map, a hybrid bundle adjustment framework is proposed, which estimates the poses of the cameras with respect to the prior laser map as well as optimizes the state variables of the online visual inertial odometry system simultaneously. For more accurate cross-modal data association, the laser map is optimized using multi-session laser and visual data to extract the salient and stable subset for localization. To validate the efficiency of the proposed method, we collect data in south part of our campus in different seasons, along the same and opposite-direction route. In all sessions of localization data, our proposed method gives satisfactory results, and shows the superiority of the hybrid bundle adjustment and map optimization.

Connected through hyperedge HE-1 (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"
    - "a minimal closed form solution for multi perspective pose estimation using points and lines"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "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-2 (induced by source paper "monocular direct sparse localization in a prior 3d surfel map"):
  with members:
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "carla an open urban driving simulator"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "deepgoal learning to drive with driving intention from human control demonstration"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "variational end to end navigation and localization"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection"
    - "end to end driving via conditional imitation learning"
    - "learning from maps visual common sense for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"):
  with members:
    - "yolov3 an incremental improvement"
    - "variational end to end navigation and localization"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "ground edge based lidar localization without a reflectivity calibration for autonomous driving"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "3d ssd learning hierarchical features from rgb d images for amodal 3d object detection"
    - "robust and precise vehicle localization based on multi sensor fusion in diverse city scenes"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised road affordances inference and learning in scenes without traffic signs"):
  with members:
    - "how shall i drive interaction modeling and motion planning towards empathetic and socially graceful driving"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "juncnet a deep neural network for road junction disambiguation for autonomous vehicles"
    - "conditional affordance learning for driving in urban environments"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "learning from maps visual common sense for autonomous driving"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "real time environmentally robust 3d lidar localization"):
  with members:
    - "a robust pavement mapping system based on normal constrained stereo visual odometry"
    - "key ingredients of self driving cars"
    - "appearance based landmark selection for efficient long term visual localization"
    - "map management for efficient long term visual localization in outdoor environments"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-7 (induced by source paper "a robust stereo camera localization method with prior lidar map constrains"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "low cost gps aided lidar state estimation and map building"
    - "pcr pro 3d sparse and different scale point clouds registration and robust estimation of information matrix for pose graph slam"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"

Connected through hyperedge HE-8 (induced by source paper "communication constrained cloud based long term visual localization in real time"):
  with members:
    - "appearance based landmark selection for efficient long term visual localization"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "on manifold preintegration for real time visual inertial odometry"
    - "visual inertial monocular slam with map reuse"

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)