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: "probabilistic articulated real time tracking for robot manipulation"
  Abstract: We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in the camera frame. This avoids the need for frame transformations when using it in combination with visual object tracking methods. #R##N#Precision is achieved by modeling and correcting biases in the joint measurements as well as inaccuracies in the robot model, such as poor extrinsic camera calibration. We make our method computationally efficient through a principled combination of Kalman filtering of the joint measurements and asynchronous depth-image updates based on the Coordinate Particle Filter. #R##N#We quantitatively evaluate our approach on a dataset recorded from a real robotic platform, annotated with ground truth from a motion capture system. We show that our approach is robust and accurate even under challenging conditions such as fast motion, significant and long-term occlusions, and time-varying biases. We release the dataset along with open-source code of our approach to allow for quantitative comparison with alternative approaches.

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 "end to end pixel based deep active inference for body perception and action"):
  with members:
    - "deep active inference as variational policy gradients"
    - "sensorimotor learning for artificial body perception"
    - "active inference body perception and action for humanoid robots"
    - "scaling active inference"
    - "multimodal representation models for prediction and control from partial information"
    - "visual reinforcement learning with imagined goals"
    - "revisiting active perception"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-3 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "from variational to deterministic autoencoders"
    - "manipulation by feel touch based control with deep predictive models"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning latent space dynamics for tactile servoing"

Connected through hyperedge HE-4 (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"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learning hierarchical control for robust in hand manipulation"):
  with members:
    - "dual arm in hand manipulation and regrasping using dexterous manipulation graphs"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "residual reinforcement learning for robot control"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "real time robust finger gaits planning under object shape and dynamics uncertainties"

Connected through hyperedge HE-6 (induced by source paper "em fusion dynamic object level slam with probabilistic data association"):
  with members:
    - "fusion volumetric object level slam"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "mask r cnn"
    - "reconstructing articulated rigged models from rgb d videos"

Connected through hyperedge HE-7 (induced by source paper "active inference body perception and action for humanoid robots"):
  with members:
    - "adaptive robot body learning and estimation through predictive coding"
    - "robots as powerful allies for the study of embodied cognition from the bottom up"
    - "visual end effector tracking using a 3d model aided particle filter for humanoid robot platforms"

Connected through hyperedge HE-8 (induced by source paper "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "learning dexterous in hand manipulation"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "state representation learning for control an overview"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "learning to represent haptic feedback for partially observable tasks"
    - "stochastic variational video prediction"

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)