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: "relocalization global optimization and map merging for monocular visual inertial slam"
  Abstract: The monocular visual-inertial system (VINS), which consists one camera and one low-cost inertial measurement unit (IMU), is a popular approach to achieve accurate 6-DOF state estimation. However, such locally accurate visual-inertial odometry is prone to drift and cannot provide absolute pose estimation. Leveraging history information to relocalize and correct drift has become a hot topic. In this paper, we propose a monocular visual-inertial SLAM system, which can relocalize camera and get the absolute pose in a previous-built map. Then 4-DOF pose graph optimization is performed to correct drifts and achieve global consistent. The 4-DOF contains x, y, z, and yaw angle, which is the actual drifted direction in the visual-inertial system. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. Current map and previous map can be merged together by the global pose graph optimization. We validate the accuracy of our system on public datasets and compare against other state-of-the-art algorithms. We also evaluate the map merging ability of our system in the large-scale outdoor environment. The source code of map reuse is integrated into our public code, VINS-Mono.

Connected through hyperedge HE-1 (induced by source paper "visual inertial navigation a concise review"):
  with members:
    - "observability aware self calibration of visual and inertial sensors for ego motion estimation"
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "continuous time visual inertial odometry for event cameras"
    - "visual inertial object detection and mapping"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "closed form solution to cooperative visual inertial structure from motion"
    - "robocentric visual inertial odometry"

Connected through hyperedge HE-2 (induced by source paper "learning whole image descriptors for real time loop detection andkidnap recovery under large viewpoint difference"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "the limits and potentials of deep learning for robotics"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "neighbourhood consensus networks"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "lightweight unsupervised deep loop closure"

Connected through hyperedge HE-3 (induced by source paper "an efficient schmidt ekf for 3d visual inertial slam"):
  with members:
    - "robocentric visual inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "lightweight unsupervised deep loop closure"
    - "visual inertial monocular slam with map reuse"
    - "consistent map based 3d localization on mobile devices"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-4 (induced by source paper "there s no place like home visual teach and repeat for emergency return of multirotor uavs during gps failure"):
  with members:
    - "uav visual teach and repeat using only semantic object features"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "from tracking to robust maneuver regulation an easy to design approach for vtol aerial robots"

Connected through hyperedge HE-5 (induced by source paper "vins mono a robust and versatile monocular visual inertial state estimator"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "visual inertial monocular slam with map reuse"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

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.CV(Computer Vision and Pattern Recognition)