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 "pointloc deep pose regressor for lidar point cloud localization"):
  Abstract: In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map. Compared to RGB image-based relocalization, LiDAR frames can provide rich and robust geometric information about a scene. However, LiDAR point clouds are sparse and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360° LiDAR input frames. Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposed method can achieve accurate relocalization performance. We show that our approach improves the state-of-the-art MapNet by 69.59% in translation and 75.70% in rotation, and AtLoc by 66.86% in translation and 78.83% in rotation on the challenging outdoor large-scale Oxford Radar RobotCar dataset.

Includes members:
  Member "robust attentional aggregation of deep feature sets for multi view 3d reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the limitations of representing functions on sets")
  Member "local supports global deep camera relocalization with sequence enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "selectfusion a generic framework to selectively learn multisensory fusion")
  Member "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "atloc attention guided camera localization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mvloc multimodal variational geometry aware learning for visual localization")
  Member "deep hough voting for 3d object detection in point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review deep learning on 3d point clouds")
  Member "deeppco end to end point cloud odometry through deep parallel neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deeptio a deep thermal inertial odometry with visual hallucination")
  Member "deep auxiliary learning for visual localization and odometry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "flownet3d learning scene flow in 3d point clouds":
    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.RO(Robotics)