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: "openseqslam2 0 an open source toolbox for visual place recognition under changing conditions"
  Abstract: Visually recognising a traversed route - regardless of whether seen during the day or night, in clear or inclement conditions, or in summer or winter - is an important capability for navigating robots. Since SeqSLAM was introduced in 2012, a large body of work has followed exploring how robotic systems can use the algorithm to meet the challenges posed by navigation in changing environmental conditions. The following paper describes OpenSeqSLAM2.0, a fully open source toolbox for visual place recognition under changing conditions. Beyond the benefits of open access to the source code, OpenSeqSLAM2.0 provides a number of tools to facilitate exploration of the visual place recognition problem and interactive parameter tuning. Using the new open source platform, it is shown for the first time how comprehensive parameter characterisations provide new insights into many of the system components previously presented in ad hoc ways and provide users with a guide to what system component options should be used under what circumstances and why.

Connected through hyperedge HE-1 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning"):
  with members:
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "learning to follow directions in street view"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "visual place recognition for aerial robotics exploring accuracy computation trade off for local image descriptors"
    - "cross view policy learning for street navigation"
    - "automatic coverage selection for surface based visual localization"
    - "the streetlearn environment and dataset"

Connected through hyperedge HE-2 (induced by source paper "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"):
  with members:
    - "openmpr recognize places using multimodal data for people with visual impairments"
    - "visual localization of key positions for visually impaired people"
    - "panorama to panorama matching for location recognition"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "on the performance of convnet features for place recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "multi process fusion visual place recognition using multiple image processing methods"):
  with members:
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "deep learning features at scale for visual place recognition"
    - "look no further adapting the localization sensory window to the temporal characteristics of the environment"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "on the performance of convnet features for place recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "openmpr recognize places using multimodal data for people with visual impairments"):
  with members:
    - "visual localization of key positions for visually impaired people"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "on the performance of convnet features for place recognition"

Connected through hyperedge HE-5 (induced by source paper "place recognition for stereo visualodometry using lidar descriptors"):
  with members:
    - "extending monocular visual odometry to stereo camera systems by scale optimization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "deep learning features at scale for visual place recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)