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: "closed loop learning of visual control policies"
  Abstract: In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-based image classifier in front of a reinforcement learning algorithm. The classifier partitions the visual space according to the presence or absence of few highly informative local descriptors that are incrementally selected in a sequence of attempts to remove perceptual aliasing. We also address the problem of fighting overfitting in such a greedy algorithm. Finally, we show how high-level visual features can be generated when the power of local descriptors is insufficient for completely disambiguating the aliased states. This is done by building a hierarchy of composite features that consist of recursive spatial combinations of visual features. We demonstrate the efficacy of our algorithms by solving three visual navigation tasks and a visual version of the classical "Car on the Hill" control problem.

Connected through hyperedge HE-1 (induced by source paper "deep predictive policy training using reinforcement learning"):
  with members:
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "self learning and adaptation in a sensorimotor framework"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "path integral guided policy search"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"

Connected through hyperedge HE-2 (induced by source paper "deep learning in neural networks"):
  with members:
    - "going deeper with convolutions"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "a clockwork rnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "sequence to sequence learning with neural networks"
    - "multi column deep neural networks for offline handwritten chinese character classification"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "efficient visual coding from retina to v2"

Connected through hyperedge HE-3 (induced by source paper "on learning to think algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models"):
  with members:
    - "deep learning in neural networks"
    - "going deeper with convolutions"
    - "neural turing machines"
    - "deep speech scaling up end to end speech recognition"
    - "a clockwork rnn"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "end to end training of deep visuomotor policies"):
  with members:
    - "deep learning in neural networks"
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"
    - "continuous control with deep reinforcement learning"
    - "deep residual learning for image recognition"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "learning contact rich manipulation skills with guided policy search"
    - "deep spatial autoencoders for visuomotor learning"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"

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.CV(Computer Vision and Pattern Recognition)