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: "appearance based landmark selection for efficient long term visual localization"
  Abstract: We present an online landmark selection method for distributed long-term visual localization systems in bandwidth-constrained environments. Sharing a common map for online localization provides a fleet of au- tonomous vehicles with the possibility to maintain and access a consistent map source, and therefore reduce redundancy while increasing efficiency. However, connectivity over a mobile network imposes strict bandwidth constraints and thus the need to minimize the amount of exchanged data. The wide range of varying appearance conditions encountered during long-term visual localization offers the potential to reduce data usage by extracting only those visual cues which are relevant at the given time. Motivated by this, we propose an unsupervised method of adaptively selecting landmarks according to how likely these landmarks are to be observable under the prevailing appear- ance condition. The ranking function this selection is based upon exploits landmark co-observability statistics collected in past traversals through the mapped area. Evaluation is per- formed over different outdoor environments, large time-scales and varying appearance conditions, including the extreme tran- sition from day-time to night-time, demonstrating that with our appearance-dependent selection method, we can significantly reduce the amount of landmarks used for localization while maintaining or even improving the localization performance.

Connected through hyperedge HE-1 (induced by source paper "experimental comparison of visual aided odometry methods for rail vehicles"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "robust odometry using sensor consensus analysis"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "visual inertial self calibration on informative motion segments"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-2 (induced by source paper "low latency visual slam with appearance enhanced local map building"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "ldso direct sparse odometry with loop closure"
    - "experiments in fast autonomous gps denied quadrotor flight"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "vizard reliable visual localization for autonomous vehicles in urban outdoor environments"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "superpoint self supervised interest point detection and description"
    - "visual inertial monocular slam with map reuse"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-4 (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"
    - "map management for efficient long term visual localization in outdoor environments"
    - "laser map aided visual inertial localization in changing environment"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "communication constrained cloud based long term visual localization in real time"):
  with members:
    - "laser map aided visual inertial localization in changing environment"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "on manifold preintegration for real time visual inertial odometry"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-6 (induced by source paper "metrics for the evaluation of localisation robustness"):
  with members:
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"

Connected through hyperedge HE-7 (induced by source paper "maplab an open framework for research in visual inertial mapping and localization"):
  with members:
    - "visual place recognition with probabilistic voting"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "topomap topological mapping and navigation based on visual slam maps"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "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.RO(Robotics)