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: "usip unsupervised stable interest point detection from 3d point clouds"
  Abstract: In this paper, we propose the USIP detector: an Unsupervised Stable Interest Point detector that can detect highly repeatable and accurately localized keypoints from 3D point clouds under arbitrary transformations without the need for any ground truth training data. Our USIP detector consists of a feature proposal network that learns stable keypoints from input 3D point clouds and their respective transformed pairs from randomly generated transformations. We provide degeneracy analysis of our USIP detector and suggest solutions to prevent it. We encourage high repeatability and accurate localization of the keypoints with a probabilistic chamfer loss that minimizes the distances between the detected keypoints from the training point cloud pairs. Extensive experimental results of repeatability tests on several simulated and real-world 3D point cloud datasets from Lidar, RGB-D and CAD models show that our USIP detector significantly outperforms existing hand-crafted and deep learning-based 3D keypoint detectors. Our code is available at the project website. this https URL

Connected through hyperedge HE-1 (induced by source paper "robust 6d object pose estimation by learning rgb d features"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "explaining the ambiguity of object detection and 6d pose from visual data"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "pvnet pixel wise voting network for 6dof pose estimation"

Connected through hyperedge HE-2 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description"):
  with members:
    - "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"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "sips succinct interest points from unsupervised inlierness probability learning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of category specific symmetric 3d keypoints from point sets"):
  with members:
    - "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"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-5 (induced by source paper "d3feat joint learning of dense detection and description of 3d local features"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "kpconv flexible and deformable convolution for point clouds"
    - "unsuperpoint end to end unsupervised interest point detector and descriptor"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "lf net learning local features from images"
    - "open3d a modern library for 3d data processing"

Connected through hyperedge HE-6 (induced by source paper "stickypillars robust feature matching on point clouds using graph neural networks"):
  with members:
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "lo net deep real time lidar odometry"
    - "superglue learning feature matching with graph neural networks"
    - "loam_livox a fast robust high precision lidar odometry and mapping package for lidars of small fov"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"

Connected through hyperedge HE-7 (induced by source paper "lcd learned cross domain descriptors for 2d 3d matching"):
  with members:
    - "2d3d matchnet learning to match keypoints across 2d image and 3d point cloud"
    - "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-8 (induced by source paper "skd unsupervised keypoint detecting for point clouds using embedded saliency estimation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "elf embedded localisation of features in pre trained cnn"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "generating 3d adversarial point clouds"

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)