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: "learning a bias correction for lidar only motion estimation"
  Abstract: This paper presents a novel technique to correct for bias in a classical estimator using a learning approach. We apply a learned bias correction to a lidar-only motion estimation pipeline. Our technique trains a Gaussian process (GP) regression model using data with ground truth. The inputs to the model are high-level features derived from the geometry of the point-clouds, and the outputs are the predicted biases between poses computed by the estimator and the ground truth. The predicted biases are applied as a correction to the poses computed by the estimator. #R##N#Our technique is evaluated on over 50km of lidar data, which includes the KITTI odometry benchmark and lidar datasets collected around the University of Toronto campus. After applying the learned bias correction, we obtained significant improvements to lidar odometry in all datasets tested. We achieved around 10% reduction in errors on all datasets from an already accurate lidar odometry algorithm, at the expense of only less than 1% increase in computational cost at run-time.

Connected through hyperedge HE-1 (induced by source paper "probabilistic egocentric motion correction of lidar point cloud and projection to camera images for moving platforms"):
  with members:
    - "automated evaluation of semantic segmentation robustness for autonomous driving"
    - "automatic extrinsic calibration between a camera and a 3d lidar using 3d point and plane correspondences"
    - "lidar point clouds correction acquired from a moving car based on can bus data"

Connected through hyperedge HE-2 (induced by source paper "mapless online detection of dynamic objects in 3d lidar"):
  with members:
    - "deep semantic classification for 3d lidar data"
    - "paris lille 3d a large and high quality ground truth urban point cloud dataset for automatic segmentation and classification"
    - "carla an open urban driving simulator"
    - "multi view 3d object detection network for autonomous driving"
    - "robust moving objects detection in lidar data exploiting visual cues"

Connected through hyperedge HE-3 (induced by source paper "a white noise on jerk motion prior for continuous time trajectory estimation on se 3"):
  with members:
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "4d crop monitoring spatio temporal reconstruction for agriculture"

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)