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: "3dfeat net weakly supervised local 3d features for point cloud registration"
  Abstract: In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. Unlike many existing works, we do not require manual annotation of matching point clusters. Instead, we leverage on alignment and attention mechanisms to learn feature correspondences from GPS/INS tagged 3D point clouds without explicitly specifying them. We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravity-aligned datasets.

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"
    - "deepmapping unsupervised map estimation from multiple point clouds"

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 "end to end learning local multi view descriptors for 3d point clouds"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "3d local features for direct pairwise registration"
    - "view n gram network for 3d object retrieval"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-4 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lo net deep real time lidar odometry"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "sips succinct interest points from unsupervised inlierness probability learning"

Connected through hyperedge HE-5 (induced by source paper "skd unsupervised keypoint detection for point clouds using saliency estimation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "point voxel cnn for efficient 3d deep learning"
    - "elf embedded localisation of features in pre trained cnn"
    - "deep closest point learning representations for point cloud registration"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-6 (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"
    - "deepmapping unsupervised map estimation from multiple point clouds"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning of category specific symmetric 3d keypoints from point sets"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "deep non rigid structure from motion"
    - "pytorch an imperative style high performance deep learning library"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-8 (induced by source paper "self supervised point set local descriptors for point cloud registration"):
  with members:
    - "non iterative one step solution for point set registration problem on pose estimation without correspondence"
    - "learning autonomous exploration and mapping with semantic vision"
    - "contextual imagined goals for self supervised robotic learning"
    - "mortonnet self supervised learning of local features in 3d point clouds"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "grasp2vec learning object representations from self supervised grasping"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"

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)