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: "2d3d matchnet learning to match keypoints across 2d image and 3d point cloud"
  Abstract: Large-scale point cloud generated from 3D sensors is more accurate than its image-based counterpart. However, it is seldom used in visual pose estimation due to the difficulty in obtaining 2D-3D image to point cloud correspondences. In this paper, we propose the 2D3D-MatchNet - an end-to-end deep network architecture to jointly learn the descriptors for 2D and 3D keypoint from image and point cloud, respectively. As a result, we are able to directly match and establish 2D-3D correspondences from the query image and 3D point cloud reference map for visual pose estimation. We create our Oxford 2D-3D Patches dataset from the Oxford Robotcar dataset with the ground truth camera poses and 2D-3D image to point cloud correspondences for training and testing the deep network. Experimental results verify the feasibility of our approach.

Connected through hyperedge HE-1 (induced by source paper "soic semantic online initialization and calibration for lidar and camera"):
  with members:
    - "spatiotemporal camera lidar calibration a targetless and structureless approach"
    - "calibnet geometrically supervised extrinsic calibration using 3d spatial transformer networks"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "reflectance intensity assisted automatic and accurate extrinsic calibration of 3d lidar and panoramic camera using a printed chessboard"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-2 (induced by source paper "lcd learned cross domain descriptors for 2d 3d matching"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "learning a local feature descriptor for 3d lidar scans"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"

Connected through hyperedge HE-3 (induced by source paper "global visual localization in lidar maps through shared 2d 3d embedding space"):
  with members:
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "cmrnet camera to lidar map registration"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "a survey of structure from motion"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "netvlad cnn architecture for weakly supervised place recognition"

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.CV(Computer Vision and Pattern Recognition)