human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning robust manipulation skills with guided policy search via generative motor reflexes"):
  Abstract: Guided Policy Search enables robots to learn control policies for complex manipulation tasks efficiently. Therein, the control policies are represented as high-dimensional neural networks which derive robot actions based on states. However, due to the small number of real-world trajectory samples in Guided Policy Search, the resulting neural networks are only robust in the neighbourhood of the trajectory distribution explored by real-world interactions. In this paper, we present a new policy representation called Generative Motor Reflexes, which is able to generate robust actions over a broader state space compared to previous methods. In contrast to prior stateaction policies, Generative Motor Reflexes map states to parameters for a state-dependent motor reflex, which is then used to derive actions. Robustness is achieved by generating similar motor reflexes for arbitrary states. We evaluate the presented method in simulated and real-world manipulation tasks, including contact-rich peg-in-hole tasks. Using this evaluation tasks, we show that policies represented as Generative Motor Reflexes lead to robust manipulation skills also outside the explored trajectory distribution with less training needs compared to previous methods. Therefore, the presented approach serves as a step towards reliable applications of reinforcement learning for manipulation.

Includes members:
  Member "learning sensor feedback models from demonstrations via phase modulated neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning generalisable coupling terms for obstacle avoidance via low dimensional geometric descriptors")
  Member "deep reinforcement learning for high precision assembly tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty")
  Member "robust deep reinforcement learning with adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "robust adversarial reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "learning feedback terms for reactive planning and control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vision based robotic grasping from object localization pose estimation grasp detection to motion planning a review")
  Member "guided policy search as approximate mirror descent":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic experience replay")
  Member "deep reinforcement learning for robotic manipulation with asynchronous off policy updates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "learning contact rich manipulation skills with guided policy search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "let s push things forward a survey on robot pushing")

Question: Please tell me which class the source paper 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)