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: "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"
  Abstract: Traditional control and task automation have been successfully demonstrated in a variety of structured, controlled environments through the use of highly specialized modeled robotic systems in conjunction with multiple sensors. However, application of autonomy in endoscopic surgery is very challenging, particularly in soft tissue work, due to the lack of high-quality images and the unpredictable, constantly deforming environment. In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control. This framework continuously collects 3D geometric information that allows for mapping of a deformable surgical field while tracking rigid instruments within the field. To achieve this, a model-based tracker is employed to localize the surgical tool with a kinematic prior in conjunction with a model-free tracker to reconstruct the deformable environment and provide an estimated point cloud as a mapping of the environment. The proposed framework was implemented on the da Vinci Surgical System in real-time with an end-effector controller where the target configurations are set and regulated through the framework. Our proposed framework successfully completed autonomous soft tissue manipulation tasks with high accuracy. The demonstration of this novel framework is promising for the future of surgical autonomy. In addition, we provide our dataset for further surgical research.

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"
    - "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"
    - "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"
    - "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"

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)