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 place recognition with probabilistic voting"
  Abstract: We propose a novel scoring concept for visual place recognition based on nearest neighbor descriptor voting and demonstrate how the algorithm naturally emerges from the problem formulation. Based on the observation that the number of votes for matching places can be evaluated using a binomial distribution model, loop closures can be detected with high precision. By casting the problem into a probabilistic framework, we not only remove the need for commonly employed heuristic parameters but also provide a powerful score to classify matching and non-matching places. We present methods for both a 2D-2D image matching and a 2D-3D landmark matching based on the above scoring. The approach maintains accuracy while being efficient enough for online application through the use of compact (low-dimensional) descriptors and fast nearest neighbor retrieval techniques. The proposed methods are evaluated on several challenging datasets in varied environments, showing state-of-the-art results with high precision and high recall.

Connected through hyperedge HE-1 (induced by source paper "large scale real time visual inertial localization revisited"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "fast and incremental loop closure detection using proximity graphs"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "convolutional neural network based image representation for visual loop closure detection"
    - "optimal data dependent hashing for approximate near neighbors"
    - "on the performance of convnet features for place recognition"
    - "an efficient index for visual search in appearance based slam"

Connected through hyperedge HE-3 (induced by source paper "maplab an open framework for research in visual inertial mapping and localization"):
  with members:
    - "appearance based landmark selection for efficient long term visual localization"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "local descriptor for robust place recognition using lidar intensity"):
  with members:
    - "robust photogeometric localization over time for map centric loop closure"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "ground edge based lidar localization without a reflectivity calibration for autonomous driving"
    - "x view graph based semantic multi view localization"
    - "segmatch segment based place recognition in 3d point clouds"

Connected through hyperedge HE-5 (induced by source paper "landmarkboost efficient visual context classifiers for robust localization"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "deep image retrieval learning global representations for image search"
    - "planet photo geolocation with convolutional neural networks"

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)