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 "cmrnet camera to lidar map registration"):
  Abstract: In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained on the working area, i.e. CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on the KITTI dataset, processing each frame independently without any tracking procedure. CMRNet achieves 0.26m and 1.05deg median localization accuracy on the sequence 00 of the odometry dataset, starting from a rough pose estimate displaced up to 3.5m and 17deg. To the best of our knowledge this is the first CNN-based approach that learns to match images from a monocular camera to a given, preexisting 3D LiDAR-map.

Includes members:
  Member "vlocnet deep multitask learning for semantic visual localization and odometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "vidloc a deep spatio temporal model for 6 dof video clip relocalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "deconvolutional networks for point cloud vehicle detection and tracking in driving scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning object bounding boxes for 3d instance segmentation on point clouds")
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "learning less is more 6d camera localization via 3d surface regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "pwc net cnns for optical flow using pyramid warping and cost volume":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "geometry aware learning of maps for camera localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization")
  Member "regnet multimodal sensor registration using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")

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)