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: "deepmapping unsupervised map estimation from multiple point clouds"
  Abstract: We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame. We use DNNs to model the highly non-convex mapping process that traditionally involves hand-crafted data association, sensor pose initialization, and global refinement. Our key novelty is that "training" these DNNs with properly defined unsupervised losses is equivalent to solving the underlying registration problem, but less sensitive to good initialization than ICP. Our framework contains two DNNs: a localization network that estimates the poses for input point clouds, and a map network that models the scene structure by estimating the occupancy status of global coordinates. This allows us to convert the registration problem to a binary occupancy classification, which can be solved efficiently using gradient-based optimization. We further show that DeepMapping can be readily extended to address the problem of Lidar SLAM by imposing geometric constraints between consecutive point clouds. Experiments are conducted on both simulated and real datasets. Qualitative and quantitative comparisons demonstrate that DeepMapping often enables more robust and accurate global registration of multiple point clouds than existing techniques. Our code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns"):
  with members:
    - "3dregnet a deep neural network for 3d point registration"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "coherent point drift networks unsupervised learning of non rigid point set registration"
    - "deep sketch based modeling of man made shapes"
    - "3dn 3d deformation network"
    - "deep closest point learning representations for point cloud registration"
    - "faim a convnet method for unsupervised 3d medical image registration"
    - "metric driven learning of correspondence weighting for 2 d 3 d image registration"

Connected through hyperedge HE-2 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "self driving cars a survey"
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "pixor real time 3d object detection from point clouds"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "3d point cloud denoising via deep neural network based local surface estimation"
    - "fvnet 3d front view proposal generation for real time object detection from point clouds"

Connected through hyperedge HE-3 (induced by source paper "learning multiview 3d point cloud registration"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "efficient and robust registration on the 3d special euclidean group"
    - "3d local features for direct pairwise registration"
    - "deep closest point learning representations for point cloud registration"
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "latent ransac"
    - "the perfect match 3d point cloud matching with smoothed densities"

Connected through hyperedge HE-4 (induced by source paper "3dregnet a deep neural network for 3d point registration"):
  with members:
    - "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "regnet learning the optimization of direct image to image pose registration"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "automatic cnn based detection of cardiac mr motion artefacts using k space data augmentation and curriculum learning"
    - "teacher student curriculum learning"

Connected through hyperedge HE-5 (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"
    - "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"
    - "hierarchical discrete distribution decomposition for match density estimation"

Connected through hyperedge HE-6 (induced by source paper "deeplocalization landmark based self localization with deep neural networks"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "environment perception framework fusing multi object tracking dynamic occupancy grid maps and digital maps"
    - "geometric loss functions for camera pose regression with deep learning"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "robust optimization for deep regression"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "can generalised relative pose estimation solve sparse 3d registration"):
  with members:
    - "imls slam scan to model matching based on 3d data"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "learning compact geometric features"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"
    - "gogma globally optimal gaussian mixture alignment"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "multi image matching via fast alternating minimization"

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)