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: "imls slam scan to model matching based on 3d data"
  Abstract: The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the robotics community, especially using mono, stereo cameras or depth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in the last 10 years to be very useful to perceive the environment in autonomous driving, but few methods exist that directly use these 3D data for odometry. We present a new low-drift SLAM algorithm based only on 3D LiDAR data. Our method relies on a scan-to-model matching framework. We first have a specific sampling strategy based on the LiDAR scans. We then define our model as the previous localized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surface representation. We show experiments with the Velodyne HDL32 with only 0.40% drift over a 4 km acquisition without any loop closure (i.e., 16 m drift after 4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 and ranked among the best methods (against mono, stereo and LiDAR methods) with a global drift of only 0.69%.

Connected through hyperedge HE-1 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lo net deep real time lidar odometry"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-2 (induced by source paper "a new approach to 3d icp covariance estimation"):
  with members:
    - "cello 3d estimating the covariance of icp in the real world"
    - "lidar measurement bias estimation via return waveform modelling in a context of 3d mapping"
    - "characterization of a rs lidar for 3d perception"
    - "on the covariance of icp based scan matching techniques"
    - "the invariant extended kalman filter as a stable observer"

Connected through hyperedge HE-3 (induced by source paper "deepicp an end to end deep neural network for 3d point cloud registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "learning depth with convolutional spatial propagation network"
    - "deeptam deep tracking and mapping"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "hierarchical discrete distribution decomposition for match density estimation"

Connected through hyperedge HE-4 (induced by source paper "lo net deep real time lidar odometry"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "lego learning edge with geometry all at once by watching videos"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "geometric loss functions for camera pose regression with deep learning"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-5 (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"
    - "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"
    - "complex urban lidar data set"
    - "oxiod the dataset for deep inertial odometry"
    - "ridi robust imu double integration"

Connected through hyperedge HE-6 (induced by source paper "an lstm network for real time odometry estimation"):
  with members:
    - "an lstm network for highway trajectory prediction"
    - "end to end race driving with deep reinforcement learning"
    - "coupled longitudinal and lateral control of a vehicle using deep learning"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "3d fully convolutional network for vehicle detection in point cloud"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-7 (induced by source paper "delio decoupled lidar odometry"):
  with members:
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "limo lidar monocular visual odometry"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "segmatch segment based place recognition in 3d point clouds"
    - "cnn for imu assisted odometry estimation using velodyne lidar"
    - "a review of mean shift algorithms for clustering"
    - "approximate k nearest neighbour based spatial clustering using k d tree"

Connected through hyperedge HE-8 (induced by source paper "can generalised relative pose estimation solve sparse 3d registration"):
  with members:
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "learning compact geometric features"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"
    - "gogma globally optimal gaussian mixture alignment"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "multi image matching via fast alternating minimization"

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)