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: "on policy learning robust to irreversible events an application to robotic in hand manipulation"
  Abstract: In this letter, we present an approach for learning in-hand manipulation skills with a low-cost, underactuated prosthetic hand in the presence of irreversible events. Our approach combines reinforcement learning based on visual perception with low-level reactive control based on tactile perception, which aims to avoid slipping. The objective of the reinforcement learning level consists not only in fulfilling the in-hand manipulation goal, but also in minimizing the intervention of the tactile reactive control. This way, the occurrence of object slipping during the learning procedure, which we consider an irreversible event, is significantly reduced. When an irreversible event occurs, the learning process is considered failed. We show the performance in two tasks, which consist in reorienting a cup and a bottle only using the fingers. The experimental results show that the proposed architecture allows reaching the goal in the Cartesian space and reduces significantly the occurrence of object slipping during the learning procedure. Moreover, without the proposed synergy between reactive control and reinforcement learning it was not possible to avoid irreversible events and, therefore, to learn the task.

Connected through hyperedge HE-1 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-2 (induced by source paper "a transfer learning approach to cross modal object recognition from visual observation to robotic haptic exploration"):
  with members:
    - "deep learning for tactile understanding from visual and haptic data"

Connected through hyperedge HE-3 (induced by source paper "learning dexterous in hand manipulation"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "distributed distributional deterministic policy gradients"
    - "robust adversarial 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)