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 object tracking using a range camera"
  Abstract: We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compute a posterior distribution over the current object pose. Depending on whether a robot or a human manipulates the object, we employ a process model with or without knowledge of control inputs. Observations are obtained from a range camera. As opposed to previous object tracking methods, we explicitly model self-occlusions and occlusions from the environment, e.g, the human or robotic hand. This leads to a strongly non-linear observation model and additional dependencies in the Bayesian network. We employ a Rao-Blackwellised particle filter to compute an estimate of the object pose at every time step. In a set of experiments, we demonstrate the ability of our method to accurately and robustly track the object pose in real-time while it is being manipulated by a human or a robot.

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 "object centric task and motion planning in dynamic environments"):
  with members:
    - "continuous relaxation of symbolic planner for one shot imitation learning"
    - "sampling based methods for factored task and motion planning"
    - "combined task and motion planning as classical ai planning"
    - "depth based object tracking using a robust gaussian filter"
    - "towards combining htn planning and geometric task planning"
    - "the fast downward planning system"

Connected through hyperedge HE-3 (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-4 (induced by source paper "depth camera based particle filter for robotic osteotomy navigation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deep 6 dof tracking"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "quaternion kinematics for the error state kalman filter"
    - "noise in structured light stereo depth cameras modeling and its applications"
    - "a new perspective and extension of the gaussian filter"

Connected through hyperedge HE-5 (induced by source paper "active learning of probabilistic movement primitives"):
  with members:
    - "adaptation and robust learning of probabilistic movement primitives"
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "a compliant underactuated hand for robust manipulation"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "active imitation learning via reduction to i i d active learning"
    - "interactive policy learning through confidence based autonomy"

Connected through hyperedge HE-6 (induced by source paper "leveraging contact forces for learning to grasp"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "grasp pose detection in point clouds"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "reinforcement learning for pivoting task"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "real time perception meets reactive motion generation"

Connected through hyperedge HE-7 (induced by source paper "real time perception meets reactive motion generation"):
  with members:
    - "riemannian motion policies"
    - "learning feedback terms for reactive planning and control"
    - "probabilistic articulated real time tracking for robot manipulation"
    - "idrm humanoid motion planning with real time end pose selection in complex environments"
    - "analysis and observations from the first amazon picking challenge"
    - "doomed direct online optimization of modeling errors in dynamics"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-8 (induced by source paper "probabilistic articulated real time tracking for robot manipulation"):
  with members:
    - "the coordinate particle filter a novel particle filter for high dimensional systems"
    - "rao blackwellised particle filtering for dynamic bayesian networks"

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)