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: "cnn for imu assisted odometry estimation using velodyne lidar"
  Abstract: We introduce a novel method for odometry estimation using convolutional neural networks from 3D LiDAR scans. The original sparse data are encoded into 2D matrices for the training of proposed networks and for the prediction. Our networks show significantly better precision in the estimation of translational motion parameters comparing with state of the art method LOAM, while achieving real-time performance. Together with IMU support, high quality odometry estimation and LiDAR data registration is realized. Moreover, we propose alternative CNNs trained for the prediction of rotational motion parameters while achieving results also comparable with state of the art. The proposed method can replace wheel encoders in odometry estimation or supplement missing GPS data, when the GNSS signal absents (e.g. during the indoor mapping). Our solution brings real-time performance and precision which are useful to provide online preview of the mapping results and verification of the map completeness in real time.

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 "segmap segment based mapping and localization using data driven descriptors"):
  with members:
    - "segmap 3d segment mapping using data driven descriptors"
    - "learning a local feature descriptor for 3d lidar scans"
    - "seeing the wood for the trees reliable localization in urban and natural environments"
    - "in defense of the triplet loss for person re identification"
    - "semantic visual localization"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "segcloud semantic segmentation of 3d point clouds"
    - "compressed voxel based mapping using unsupervised learning"

Connected through hyperedge HE-3 (induced by source paper "two stream networks for self supervised ego motion estimation"):
  with members:
    - "beyond photometric loss for self supervised ego motion estimation"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "lo net deep real time lidar odometry"
    - "revisiting self supervised visual representation learning"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "robustness meets deep learning an end to end hybrid pipeline for unsupervised learning of egomotion"

Connected through hyperedge HE-4 (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"
    - "imls slam scan to model matching based on 3d data"

Connected through hyperedge HE-5 (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"
    - "imls slam scan to model matching based on 3d data"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "oreos oriented recognition of 3d point clouds in outdoor scenarios"):
  with members:
    - "segmap 3d segment mapping using data driven descriptors"
    - "a comprehensive review of 3d point cloud descriptors"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "segmatch segment based place recognition in 3d point clouds"
    - "locnet global localization in 3d point clouds for mobile vehicles"
    - "image similarity using deep cnn and curriculum learning"
    - "vehicle detection from 3d lidar using fully convolutional network"

Connected through hyperedge HE-7 (induced by source paper "delio decoupled lidar odometry"):
  with members:
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "imls slam scan to model matching based on 3d data"
    - "limo lidar monocular visual odometry"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "segmatch segment based place recognition in 3d point clouds"
    - "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 "sensor aware lidar odometry"):
  with members:
    - "deeplo geometry aware deep lidar odometry"
    - "practical optimal registration of terrestrial lidar scan pairs"

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)