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: "guided policy search with delayed senor measurements"
  Abstract: Guided policy search is a method for reinforcement learning that trains a general policy for accomplishing a given task by guiding the learning of the policy with multiple guiding distributions. Guided policy search relies on learning an underlying dynamical model of the environment and then, at each iteration of the algorithm, using that model to gradually improve the policy. This model, though, often makes the assumption that the environment dynamics are markovian, e.g., depend only on the current state and control signal. In this paper we apply guided policy search to a problem with non-markovian dynamics. Specifically, we apply it to the problem of pouring a precise amount of liquid from a cup into a bowl, where many of the sensor measurements experience non-trivial amounts of delay. We show that, with relatively simple state augmentation, guided policy search can be extended to non-markovian dynamical systems, where the non-markovianess is caused by delayed sensor readings.

Connected through hyperedge HE-1 (induced by source paper "few shot goal inference for visuomotor learning and planning"):
  with members:
    - "universal planning networks"
    - "multi task maximum entropy inverse reinforcement learning"
    - "reward learning from narrated demonstrations"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "self supervised visual planning with temporal skip connections"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "visual closed loop control for pouring liquids"):
  with members:
    - "lstm a search space odyssey"
    - "multi sensor surface analysis for robotic ironing"
    - "towards learning to perceive and reason about liquids"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"
    - "learning to place new objects in a scene"

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)