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: "robot autonomy for surgery"
  Abstract: Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots.

Connected through hyperedge HE-1 (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"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"

Connected through hyperedge HE-2 (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-3 (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"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (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"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-5 (induced by source paper "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "open sourced reinforcement learning environments for surgical robotics"
    - "augmented reality predictive displays to help mitigate the effects of delayed telesurgery"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "robust estimation of similarity transformation for visual object tracking"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "real time 3d tracking of articulated tools for robotic surgery"
    - "dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture"

Connected through hyperedge HE-6 (induced by source paper "adversarial imitation via variational inverse reinforcement learning"):
  with members:
    - "intrinsically motivated reinforcement learning for human robot interaction in the real world"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "trust region policy optimization"

Connected through hyperedge HE-7 (induced by source paper "augmented reality predictive displays to help mitigate the effects of delayed telesurgery"):
  with members:
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"
    - "real time 3d tracking of articulated tools for robotic surgery"

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)