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: "segmentation driven 6d object pose estimation"
  Abstract: The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm. In both cases, the object is treated as a global entity, and a single pose estimate is computed. As a consequence, the resulting techniques can be vulnerable to large occlusions. #R##N#In this paper, we introduce a segmentation-driven 6D pose estimation framework where each visible part of the objects contributes a local pose prediction in the form of 2D keypoint locations. We then use a predicted measure of confidence to combine these pose candidates into a robust set of 3D-to-2D correspondences, from which a reliable pose estimate can be obtained. We outperform the state-of-the-art on the challenging Occluded-LINEMOD and YCB-Video datasets, which is evidence that our approach deals well with multiple poorly-textured objects occluding each other. Furthermore, it relies on a simple enough architecture to achieve real-time performance.

Connected through hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "disentangling monocular 3d object detection"
    - "multi path learning for object pose estimation across domains"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "mesh r cnn"
    - "normalized object coordinate space for category level 6d object pose and size estimation"

Connected through hyperedge HE-2 (induced by source paper "robust 6d object pose estimation by learning rgb d features"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "mobilepose real time pose estimation for unseen objects with weak shape supervision"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "hybridpose 6d object pose estimation under hybrid representations"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "object as distribution"
    - "mediapipe a framework for building perception pipelines"
    - "centernet keypoint triplets for object detection"
    - "implicit 3d orientation learning for 6d object detection from rgb images"

Connected through hyperedge HE-4 (induced by source paper "deepurl deep pose estimation framework for underwater relative localization"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "beyond sharing weights for deep domain adaptation"
    - "cullnet calibrated and pose aware confidence scores for object pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "yolov3 an incremental improvement"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"

Connected through hyperedge HE-5 (induced by source paper "6dof object pose estimation via differentiable proxy voting loss"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd single shot multibox detector"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "vision based robotic grasping from object localization pose estimation grasp detection to motion planning a review"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pixor real time 3d object detection from point clouds"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "bop benchmark for 6d object pose estimation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"

Connected through hyperedge HE-7 (induced by source paper "accurate 6d object pose estimation by pose conditioned mesh reconstruction"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "generating 3d faces using convolutional mesh autoencoders"

Connected through hyperedge HE-8 (induced by source paper "single stage 6d object pose estimation"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "neural guided ransac learning where to sample model hypotheses"
    - "dpod 6d pose object detector and refiner"
    - "backpropagation friendly eigendecomposition"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "lf net learning local features from images"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "yolov3 an incremental improvement"

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)