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: "deep model based 6d pose refinement in rgb"
  Abstract: We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model. In contrast to previous work our method is correspondence-free, segmentation-free, can handle occlusion and is agnostic to geometrical symmetry as well as visual ambiguities. Additionally, we observe a strong robustness towards rough initialization. The approach can run in real-time and produces pose accuracies that come close to 3D ICP without the need for depth data. Furthermore, our networks are trained from purely synthetic data and will be published together with the refinement code to ensure reproducibility.

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 "deepim deep iterative matching for 6d pose estimation"):
  with members:
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "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"
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "geometric loss functions for camera pose regression with deep learning"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "deep 6 dof tracking"

Connected through hyperedge HE-3 (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"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-4 (induced by source paper "deceptionnet network driven domain randomization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "dpod 6d pose object detector and refiner"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning to adapt structured output space for semantic segmentation"
    - "learning dexterous in hand manipulation"
    - "adversarial patch"
    - "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"

Connected through hyperedge HE-5 (induced by source paper "implicit 3d orientation learning for 6d object detection from rgb images"):
  with members:
    - "bop benchmark for 6d object pose estimation"
    - "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-6 (induced by source paper "multi path learning for object pose estimation across domains"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "6d pose estimation with correlation fusion"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "recurrent convolutional fusion for rgb d object recognition"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "segmentation driven 6d object pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "densefusion 6d object pose estimation by iterative dense fusion"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "a unified framework for multi view multi class object pose estimation"
    - "6d pose estimation using an improved method based on point pair features"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"

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)