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: "maplab an open framework for research in visual inertial mapping and localization"
  Abstract: Robust and accurate visual-inertial estimation is crucial to many of today's challenges in robotics. Being able to localize against a prior map and obtain accurate and drift-free pose estimates can push the applicability of such systems even further. Most of the currently available solutions, however, either focus on a single session use case, lack localization capabilities, or do not provide an end-to-end pipeline. We believe that only a complete system, combining state-of-the-art algorithms, scalable multisession mapping tools, and a flexible user interface, can become an efficient research platform. We, therefore, present maplab, an open, research-oriented visual-inertial mapping framework for processing and manipulating multisession maps, written in C++. On the one hand, maplab can be seen as a ready-to-use visual-inertial mapping and localization system. On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure. Furthermore, it includes an online frontend that can create visual-inertial maps and also track a global drift-free pose within a localization map. In this letter, we present the system architecture, five use cases, and evaluations of the system on public datasets. The source code of maplab is freely available for the benefit of the robotics research community.

Connected through hyperedge HE-1 (induced by source paper "whole body control of a mobile manipulator using end to end reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "object finding in cluttered scenes using interactive perception"
    - "deep value model predictive control"
    - "whole body mpc for a dynamically stable mobile manipulator"
    - "sim to real learning agile locomotion for quadruped robots"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "nonlinear model predictive guidance for fixed wing uavs using identified control augmented dynamics"
    - "comparing task simplifications to learn closed loop object picking using deep reinforcement learning"

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"
    - "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"
    - "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-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"
    - "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"
    - "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-4 (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-5 (induced by source paper "large scale real time visual inertial localization revisited"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "identifying robust landmarks in feature based maps"
    - "hybrid scene compression for visual localization"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "3d visual perception for self driving cars using a multi camera system calibration mapping localization and obstacle detection"

Connected through hyperedge HE-6 (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"
    - "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"
    - "relocalization global optimization and map merging for monocular visual inertial slam"

Connected through hyperedge HE-7 (induced by source paper "predicting unobserved space for planning via depth map augmentation"):
  with members:
    - "depth coefficients for depth completion"
    - "fusion of stereo and still monocular depth estimates in a self supervised learning context"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"

Connected through hyperedge HE-8 (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"
    - "neighbourhood consensus networks"
    - "relocalization global optimization and map merging for monocular visual inertial slam"
    - "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"

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)