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: "bop benchmark for 6d object pose estimation"
  Abstract: We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at this http 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"
    - "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"
    - "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 "template based unseen instance detection"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deceptionnet network driven domain randomization"
    - "learning to track any object"
    - "on the convergence of adam and beyond"
    - "photorealistic image synthesis for object instance detection"
    - "target aware deep tracking"
    - "deep model based 6d pose refinement in rgb"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-3 (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"
    - "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"
    - "a survey on joint object detection and pose estimation using monocular vision"

Connected through hyperedge HE-4 (induced by source paper "active 6d multi object pose estimation in cluttered scenarios with deep reinforcement learning"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "instance and category level 6d object pose estimation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "deep object centric policies for autonomous driving"

Connected through hyperedge HE-5 (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-6 (induced by source paper "p 2 gnet pose guided point cloud generating networks for 6 dof object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "a unified framework for multi view multi class object pose estimation"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "non local neural networks"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-7 (induced by source paper "implicit 3d orientation learning for 6d object detection from rgb images"):
  with members:
    - "deep model based 6d pose refinement in rgb"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "6d pose estimation using an improved method based on point pair features"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "3d pose regression using convolutional neural networks"
    - "domain adaptation for visual applications a comprehensive survey"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "learning to match templates for unseen instance detection"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deceptionnet network driven domain randomization"
    - "learning to track any object"
    - "on the convergence of adam and beyond"
    - "photorealistic image synthesis for object instance detection"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "fast online object tracking and segmentation a unifying approach"
    - "target driven instance detection"

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)