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: "real time object pose estimation with pose interpreter networks"
  Abstract: In this work, we introduce pose interpreter networks for 6-DoF object pose estimation. In contrast to other CNN-based approaches to pose estimation that require expensively annotated object pose data, our pose interpreter network is trained entirely on synthetic pose data. We use object masks as an intermediate representation to bridge real and synthetic. We show that when combined with a segmentation model trained on RGB images, our synthetically trained pose interpreter network is able to generalize to real data. Our end-to-end system for object pose estimation runs in real-time (20 Hz) on live RGB data, without using depth information or ICP refinement.

Connected through hyperedge HE-1 (induced by source paper "deephmap combined projection grouping and correspondence learning for full dof pose estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "physics based scene level reasoning for object pose estimation in clutter"
    - "robust 6d object pose estimation in cluttered scenes using semantic segmentation and pose regression networks"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "multivariate uncertainty in deep learning"):
  with members:
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "long short term memory kalman filters recurrent neural estimators for pose regularization"
    - "deepvo a deep learning approach for monocular visual odometry"
    - "backprop kf learning discriminative deterministic state estimators"
    - "deep residual learning for image recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "monte carlo tree search for efficient visually guided rearrangement planning"):
  with members:
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "rearrangement with nonprehensile manipulation using deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "proximal policy optimization algorithms"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

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.RO(Robotics)