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: "open sourced reinforcement learning environments for surgical robotics"
  Abstract: Reinforcement Learning (RL) is a machine learning framework for artificially intelligent systems to solve a variety of complex problems. Recent years has seen a surge of successes solving challenging games and smaller domain problems, including simple though non-specific robotic manipulation and grasping tasks. Rapid successes in RL have come in part due to the strong collaborative effort by the RL community to work on common, open-sourced environment simulators such as OpenAI's Gym that allow for expedited development and valid comparisons between different, state-of-art strategies. In this paper, we aim to bridge the RL and the surgical robotics communities by presenting the first open-sourced reinforcement learning environments for surgical robotics, called dVRL. Through the proposed RL environment, which are functionally equivalent to Gym, we show that it is easy to prototype and implement state-of-art RL algorithms on surgical robotics problems that aim to introduce autonomous robotic precision and accuracy to assisting, collaborative, or repetitive tasks during surgery. Learned policies are furthermore successfully transferable to a real robot. Finally, combining dVRL with the over 40+ international network of da Vinci Surgical Research Kits in active use at academic institutions, we see dVRL as enabling the broad surgical robotics community to fully leverage the newest strategies in reinforcement learning, and for reinforcement learning scientists with no knowledge of surgical robotics to test and develop new algorithms that can solve the real-world, high-impact challenges in autonomous surgery.

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"
    - "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"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

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"
    - "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"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "leveraging vision and kinematics data to improve realism of biomechanic soft tissue simulation for robotic surgery"):
  with members:
    - "simulation of hyperelastic materials in real time using deep learning"
    - "autonomous tissue manipulation via surgical robot using learning based model predictive control"
    - "dense depth estimation in monocular endoscopy with self supervised learning methods"
    - "learning soft tissue behavior of organs for surgical navigation with convolutional neural networks"
    - "deformable models for surgical simulation a survey"
    - "deep visual foresight for planning robot motion"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (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"
    - "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"
    - "robot autonomy for surgery"
    - "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-5 (induced by source paper "a convex optimization based dynamic model identification package for the da vinci research kit"):
  with members:
    - "linear matrix inequalities for physically consistent inertial parameter identification a statistical perspective on the mass distribution"
    - "identification of fully physical consistent inertial parameters using optimization on manifolds"

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)