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: "complex urban lidar data set"
  Abstract: This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted on both sides for maximal coverage. One 2D LiDAR faces backward while the other faces forwards to collect data of roads and buildings, respectively. Raw sensor data from Fiber Optic Gyro (FOG), Inertial Measurement Unit (IMU), and the Global Positioning System (GPS) are presented in a file format for vehicle pose estimation. The pose information of the vehicle estimated at 100 Hz is also presented after applying the graph simultaneous localization and mapping (SLAM) algorithm. For the convenience of development, the file player and data viewer in Robot Operating System (ROS) environment were also released via the web page. The full data sets are available at: this http URL In this website, 3D preview of each data set is provided using WebGL.

Connected through hyperedge HE-1 (induced by source paper "rins w robust inertial navigation system on wheels"):
  with members:
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "imls slam scan to model matching based on 3d data"
    - "using inertial sensors for position and orientation estimation"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "lstm based zero velocity detection for robust inertial navigation"
    - "deep learning based speed estimation for constraining strapdown inertial navigation on smartphones"
    - "oxiod the dataset for deep inertial odometry"
    - "ridi robust imu double integration"

Connected through hyperedge HE-2 (induced by source paper "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"):
  with members:
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "tightly coupled 3d lidar inertial odometry and mapping"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "elastic lidar fusion dense map centric continuous time slam"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-4 (induced by source paper "road is enough extrinsic calibration of non overlapping stereo camera and lidar using road information"):
  with members:
    - "calibnet geometrically supervised extrinsic calibration using 3d spatial transformer networks"

Connected through hyperedge HE-5 (induced by source paper "a 2d laser rangefinder scans dataset of standard eur pallets"):
  with members:
    - "a dataset for improved rgbd based object detection and pose estimation for warehouse pick and place"
    - "faster r cnn towards real time object detection with region proposal 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)