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: "visual inertial monocular slam with map reuse"
  Abstract: In recent years there have been excellent results in visual-inertial odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness. However, these approaches lack the capability to close loops and trajectory estimation accumulates drift even if the sensor is continually revisiting the same place. In this letter, we present a novel tightly coupled visual-inertial simultaneous localization and mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas. While our approach can be applied to any camera configuration, we address here the most general problem of a monocular camera, with its well-known scale ambiguity. We also propose a novel IMU initialization method, which computes the scale, the gravity direction, the velocity, and gyroscope and accelerometer biases, in a few seconds with high accuracy. We test our system in the 11 sequences of a recent micro-aerial vehicle public dataset achieving a typical scale factor error of 1% and centimeter precision. We compare to the state-of-the-art in visual-inertial odometry in sequences with revisiting, proving the better accuracy of our method due to map reuse and no drift accumulation.

Connected through hyperedge HE-1 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation"):
  with members:
    - "badgr an autonomous self supervised learning based navigation system"
    - "kidnapped radar topological radar localisation using rotationally invariant metric learning"
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "beyond photometric loss for self supervised ego motion estimation"
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "self supervised learning of depth and motion under photometric inconsistency"

Connected through hyperedge HE-2 (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"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "keyframe based monocular slam"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-3 (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"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "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-4 (induced by source paper "inertial only optimization for visual inertial initialization"):
  with members:
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "fast and robust initialization for visual inertial slam"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "direct visual inertial ego motion estimation via iterated extended kalman filter"):
  with members:
    - "a quadrotor with an origami inspired protective mechanism"
    - "vins mono a robust and versatile monocular visual inertial state estimator"

Connected through hyperedge HE-6 (induced by source paper "selfvio self supervised deep monocular visual inertial odometry and depth estimation"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "deeptio a deep thermal inertial odometry with visual hallucination"
    - "temporal interpolation as an unsupervised pretraining task for optical flow estimation"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "vision aided absolute trajectory estimation using an unsupervised deep network with online error correction"

Connected through hyperedge HE-7 (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-8 (induced by source paper "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "vision aided absolute trajectory estimation using an unsupervised deep network with online error correction"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "pyramid stereo matching network"
    - "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"

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)