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: "autonomous mobile robot navigation in uneven and unstructured indoor environments"
  Abstract: Robots are increasingly operating in indoor environments designed for and shared with people. However, robots working safely and autonomously in uneven and unstructured environments still face great challenges. Many modern indoor environments are designed with wheelchair accessibility in mind. This presents an opportunity for wheeled robots to navigate through sloped areas while avoiding staircases. In this paper, we present an integrated software and hardware system for autonomous mobile robot navigation in uneven and unstructured indoor environments. This modular and reusable software framework incorporates capabilities of perception and navigation. Our robot first builds a 3D OctoMap representation for the uneven environment with the 3D mapping using wheel odometry, 2D laser and RGB-D data. Then we project multilayer 2D occupancy maps from OctoMap to generate the the traversable map based on layer differences. The safe traversable map serves as the input for efficient autonomous navigation. Furthermore, we employ a variable step size Rapidly Exploring Random Trees that could adjust the step size automatically, eliminating tuning step sizes according to environments. We conduct extensive experiments in simulation and real-world, demonstrating the efficacy and efficiency of our system.

Connected through hyperedge HE-1 (induced by source paper "mobile robot localisation and navigation using lego nxt and ultrasonic sensor"):
  with members:
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision based lane detection system"
    - "real time stereo vision for road surface 3 d reconstruction"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "exploiting points and lines in regression forests for rgb d camera relocalization"):
  with members:
    - "a two point method for ptz camera calibration in sports"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "backtracking regression forests for accurate camera relocalization"
    - "image based localization using lstms for structured feature correlation"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "modelling uncertainty in deep learning for camera relocalization"

Connected through hyperedge HE-3 (induced by source paper "backtracking regression forests for accurate camera relocalization"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "image based localization using lstms for structured feature correlation"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "you only look once unified real time object detection"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

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)