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: "hbst a hamming distance embedding binary search tree for feature based visual place recognition"
  Abstract: Reliable and efficient visual place recognition is a major building block of modern SLAM systems. Leveraging on our prior work, in this letter, we present a Hamming distance embedding binary search tree (HBST) approach for binary descriptor matching and image retrieval. HBST allows for descriptor search and insertion in logarithmic time by exploiting particular properties of binary descriptors. We support the idea behind our search structure with a thorough analysis on the exploited descriptor properties and their effects on completeness and complexity of search and insertion. To validate our claims, we conducted comparative experiments for HBST and several state-of-the-art methods on a broad range of publicly available datasets. HBST is available as a compact open-source C++ header-only library.

Connected through hyperedge HE-1 (induced by source paper "fast compact and highly scalable visual place recognition through sequence based matching of overloaded representations"):
  with members:
    - "a hybrid compact neural architecture for visual place recognition"
    - "btel a binary tree encoding approach for visual localization"
    - "link and code fast indexing with graphs and compact regression codes"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "learning to index for nearest neighbor search"
    - "billion scale similarity search with gpus"
    - "x view graph based semantic multi view localization"
    - "netvlad cnn architecture for weakly supervised place recognition"

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"
    - "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 "adding cues to binary feature descriptors for visual place recognition"):
  with members:
    - "place recognition an overview of vision perspective"
    - "proslam graph slam from a programmer s perspective"
    - "polysemous codes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)