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: "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
  Abstract: Automating precision subtasks such as debridement (removing dead or diseased tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci Research Kit (dVRK) is challenging due to inherent non-linearities in cable-driven systems. We propose and evaluate a novel two-phase coarse-to-fine calibration method. In Phase I (coarse), we place a red calibration marker on the end effector and let it randomly move through a set of open-loop trajectories to obtain a large sample set of camera pixels and internal robot end-effector configurations. This coarse data is then used to train a Deep Neural Network (DNN) to learn the coarse transformation bias. In Phase II (fine), the bias from Phase I is applied to move the end-effector toward a small set of specific target points on a printed sheet. For each target, a human operator manually adjusts the end-effector position by direct contact (not through teleoperation) and the residual compensation bias is recorded. This fine data is then used to train a Random Forest (RF) to learn the fine transformation bias. Subsequent experiments suggest that without calibration, position errors average 4.55mm. Phase I can reduce average error to 2.14mm and the combination of Phase I and Phase II can reduces average error to 1.08mm. We apply these results to debridement of raisins and pumpkin seeds as fragment phantoms. Using an endoscopic stereo camera with standard edge detection, experiments with 120 trials achieved average success rates of 94.5%, exceeding prior results with much larger fragments (89.4%) and achieving a speedup of 2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source code, data, and videos are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor"):
  with members:
    - "a grasping centered analysis for cloth manipulation"
    - "model based behavioral cloning with future image similarity learning"
    - "learning to manipulate deformable objects without demonstrations"
    - "dynamic cloth manipulation with deep reinforcement learning"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "deep haptic model predictive control for robot assisted dressing"

Connected through hyperedge HE-2 (induced by source paper "super deep a surgical perception framework for robotic tissue manipulation using deep learning for feature extraction"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "open sourced reinforcement learning environments for surgical robotics"
    - "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"
    - "mis slam real time large scale dense deformable slam system in minimal invasive surgery based on heterogeneous computing"
    - "augmented reality predictive displays to help mitigate the effects of delayed telesurgery"
    - "pyramid stereo matching network"
    - "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "applying depth sensing to automated surgical manipulation with a da vinci robot"):
  with members:
    - "autonomous tissue manipulation via surgical robot using learning based model predictive control"
    - "deep imitation learning of sequential fabric smoothing policies"
    - "safety augmented value estimation from demonstrations saved safe deep model based rl for sparse cost robotic tasks"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "open sourced reinforcement learning environments for surgical robotics"
    - "toward synergic learning for autonomous manipulation of deformable tissues via surgical robots an approximate q learning approach"
    - "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"

Connected through hyperedge HE-4 (induced by source paper "efficiently calibrating cable driven surgical robots with rgbd sensing temporal windowing and linear and recurrent neural network compensation"):
  with members:
    - "applying depth sensing to automated surgical manipulation with a da vinci robot"
    - "autonomous tissue manipulation via surgical robot using learning based model predictive control"
    - "deep imitation learning of sequential fabric smoothing policies"
    - "real time data driven precision estimator for raven ii surgical robot end effector position"
    - "robot autonomy for surgery"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-5 (induced by source paper "extending deep model predictive control with safety augmented value estimation from demonstrations"):
  with members:
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "sample based learning model predictive control for linear uncertain systems"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "playing hard exploration games by watching youtube"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-6 (induced by source paper "on policy robot imitation learning from a converging supervisor"):
  with members:
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "dual policy iteration"
    - "model ensemble trust region policy optimization"
    - "addressing function approximation error in actor critic methods"
    - "convergence of value aggregation for imitation learning"

Connected through hyperedge HE-7 (induced by source paper "safety augmented value estimation from demonstrations saved safe deep model based rl for sparse cost robotic tasks"):
  with members:
    - "on policy robot imitation learning from a converging supervisor"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "hindsight experience replay"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-8 (induced by source paper "open sourced reinforcement learning environments for surgical robotics"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "a convex optimization based dynamic model identification package for the da vinci research kit"
    - "motion scaling solutions for improved performance in high delay surgical teleoperation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "hindsight experience replay"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "overcoming exploration in reinforcement learning with demonstrations"

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)