human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description"):
  Abstract: As an important technology in 3D mapping, autonomous driving, and robot navigation, LiDAR odometry is still a challenging task. Appropriate data structure and unsupervised deep learning are the keys to achieve an easy adjusted LiDAR odometry solution with high performance. Utilizing compact 2D structured spherical ring projection model and voxel model which preserves the original shape of input data, we propose a fully unsupervised Convolutional Auto-Encoder based LiDAR Odometry (CAE-LO) that detects interest points from spherical ring data using 2D CAE and extracts features from multi-resolution voxel model using 3D CAE. We make several key contributions: 1) experiments based on KITTI dataset show that our interest points can capture more local details to improve the matching success rate on unstructured scenarios and our features outperform state-of-the-art by more than 50% in matching inlier ratio; 2) besides, we also propose a keyframe selection method based on matching pairs transferring, an odometry refinement method for keyframes based on extended interest points from spherical rings, and a backward pose update method. The odometry refinement experiments verify the proposed ideas' feasibility and effectiveness.

Includes members:
  Member "usip unsupervised stable interest point detection from 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "deepicp an end to end deep neural network for 3d point cloud registration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a rotation invariant framework for deep point cloud analysis")
  Member "key net keypoint detection by handcrafted and learned cnn filters":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "under the radar learning to predict robust keypoints for odometry estimation and metric localisation in radar")
  Member "lo net deep real time lidar odometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stickypillars robust feature matching on point clouds using graph neural networks")
  Member "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "ppfnet global context aware local features for robust 3d point matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "the perfect match 3d point cloud matching with smoothed densities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds")
  Member "3dfeat net weakly supervised local 3d features for point cloud registration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")

Question: Please tell me which class the source paper 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.CV(Computer Vision and Pattern Recognition)