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: "rotational subgroup voting and pose clustering for robust 3d object recognition"
  Abstract: It is possible to associate a highly constrained subset of relative 6 DoF poses between two 3D shapes, as long as the local surface orientation, the normal vector, is available at every surface point. Local shape features can be used to find putative point correspondences between the models due to their ability to handle noisy and incomplete data. However, this correspondence set is usually contaminated by outliers in practical scenarios, which has led to many past contributions based on robust detectors such as the Hough transform or RANSAC. The key insight of our work is that a single correspondence between oriented points on the two models is constrained to cast votes in a 1 DoF rotational subgroup of the full group of poses, SE(3). Kernel density estimation allows combining the set of votes efficiently to determine a full 6 DoF candidate pose between the models. This modal pose with the highest density is stable under challenging conditions, such as noise, clutter, and occlusions, and provides the output estimate of our method. We first analyze the robustness of our method in relation to noise and show that it handles high outlier rates much better than RANSAC for the task of 6 DoF pose estimation. We then apply our method to four state of the art data sets for 3D object recognition that contain occluded and cluttered scenes. Our method achieves perfect recall on two LIDAR data sets and outperforms competing methods on two RGB-D data sets, thus setting a new standard for general 3D object recognition using point cloud data.

Connected through hyperedge HE-1 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands"):
  with members:
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "scene level pose estimation for multiple instances of densely packed objects"
    - "learning joint reconstruction of hands and manipulated objects"
    - "manipulation by feel touch based control with deep predictive models"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "principal components of touch"

Connected through hyperedge HE-2 (induced by source paper "honnotate a method for 3d annotation of hand and objects poses"):
  with members:
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-3 (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"
    - "bop benchmark for 6d object pose estimation"
    - "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"

Connected through hyperedge HE-4 (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 model based 6d pose refinement in rgb"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "maskedfusion mask based 6d object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "panoptic segmentation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "real time object pose estimation with pose interpreter networks"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "l3doc lifelong 3d object classification"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "deep virtual networks for memory efficient inference of multiple tasks"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "lifelong learning with dynamically expandable networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "continual learning through synaptic intelligence"
    - "icarl incremental classifier and representation learning"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-7 (induced by source paper "maskedfusion mask based 6d object pose detection"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "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"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "cascade r cnn delving into high quality object detection"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"

Connected through hyperedge HE-8 (induced by source paper "pointposenet accurate object detection and 6 dof pose estimation in point clouds"):
  with members:
    - "relation shape convolutional neural network for point cloud analysis"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "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 6d making rgb based 3d detection and 6d pose estimation great again"

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)