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: "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
  Abstract: Tracking 6D poses of objects from videos provides rich information to a robot in performing different tasks such as manipulation and navigation. In this work, we formulate the 6D object pose tracking problem in the Rao-Blackwellized particle filtering framework, where the 3D rotation and the 3D translation of an object are decoupled. This factorization allows our approach, called PoseRBPF, to efficiently estimate the 3D translation of an object along with the full distribution over the 3D rotation. This is achieved by discretizing the rotation space in a fine-grained manner, and training an auto-encoder network to construct a codebook of feature embeddings for the discretized rotations. As a result, PoseRBPF can track objects with arbitrary symmetries while still maintaining adequate posterior distributions. Our approach achieves state-of-the-art results on two 6D pose estimation benchmarks. A video showing the experiments can be found at this https URL

Connected through hyperedge HE-1 (induced by source paper "deepim deep iterative matching for 6d pose estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "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-2 (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"
    - "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"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-3 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "non smooth newton methods for deformable multi body dynamics"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"

Connected through hyperedge HE-4 (induced by source paper "latentfusion end to end differentiable reconstruction and rendering for unseen object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "hologan unsupervised learning of 3d representations from natural images"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "multi path learning for object pose estimation across domains"
    - "photoshape photorealistic materials for large scale shape collections"
    - "neural volumes learning dynamic renderable volumes from images"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"

Connected through hyperedge HE-5 (induced by source paper "6 pack category level 6d pose tracker with anchor based keypoints"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "cornernet lite efficient keypoint based object detection"
    - "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"
    - "open3d a modern library for 3d data processing"
    - "deep object pose estimation for semantic robotic grasping of household objects"

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"
    - "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"
    - "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 "self supervised 6d object pose estimation for robot manipulation"):
  with members:
    - "a billion ways to grasp an evaluation of grasp sampling schemes on a dense physics based grasp data set"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "learning instance segmentation by interaction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

Connected through hyperedge HE-8 (induced by source paper "verefine integrating object pose verification with iterative physics guided refinement"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "graspa 1 0 graspa is a robot arm grasping performance benchmark"
    - "dpod 6d pose object detector and refiner"
    - "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"

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)