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: "open3d a modern library for 3d data processing"
  Abstract: Open3D is an open-source library that supports rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. The backend is highly optimized and is set up for parallelization. Open3D was developed from a clean slate with a small and carefully considered set of dependencies. It can be set up on different platforms and compiled from source with minimal effort. The code is clean, consistently styled, and maintained via a clear code review mechanism. Open3D has been used in a number of published research projects and is actively deployed in the cloud. We welcome contributions from the open-source community.

Connected through hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "albumentations fast and flexible image augmentations"
    - "how much position information do convolutional neural networks encode"
    - "disentangling monocular 3d object detection"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "6d object pose estimation based on 2d bounding box"

Connected through hyperedge HE-2 (induced by source paper "teaser fast and certifiable point cloud registration"):
  with members:
    - "did you miss the sign a false negative alarm system for traffic sign detectors"
    - "consensus maximization tree search revisited"
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "a practical maximum clique algorithm for matching with pairwise constraints"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "prnet self supervised learning for partial to partial registration"
    - "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"

Connected through hyperedge HE-3 (induced by source paper "6d object pose regression via supervised learning on point clouds"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "occlusion resistant object rotation regression from point cloud segments"
    - "when regression meets manifold learning for object recognition and pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "3d pose regression using convolutional neural networks"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-4 (induced by source paper "d3feat joint learning of dense detection and description of 3d local features"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "robotic grasping through combined image based grasp proposal and 3d reconstruction"):
  with members:
    - "kpam sc generalizable manipulation planning using keypoint affordance and shape completion"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pixels to plans learning non prehensile manipulation by imitating a planner"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "higher order function networks for learning composable 3d object representations"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-6 (induced by source paper "yoloff you only learn offsets for robust 6dof object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "3d pose regression using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "toward autonomous robotic micro suturing using optical coherence tomography calibration and path planning"):
  with members:
    - "towards robotic eye surgery marker free online hand eye calibration using optical coherence tomography images"

Connected through hyperedge HE-8 (induced by source paper "kimera an open source library for real time metric semantic localization and mapping"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "dual graph convolutional network for semantic segmentation"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "incremental visual inertial 3d mesh generation with structural regularities"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "active scene understanding via online semantic reconstruction"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"

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)