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: "compatible value gradients for reinforcement learning of continuous deep policies"
  Abstract: This paper proposes GProp, a deep reinforcement learning algorithm for continuous policies with compatible function approximation. The algorithm is based on two innovations. Firstly, we present a temporal-difference based method for learning the gradient of the value-function. Secondly, we present the deviator-actor-critic (DAC) model, which comprises three neural networks that estimate the value function, its gradient, and determine the actor's policy respectively. We evaluate GProp on two challenging tasks: a contextual bandit problem constructed from nonparametric regression datasets that is designed to probe the ability of reinforcement learning algorithms to accurately estimate gradients; and the octopus arm, a challenging reinforcement learning benchmark. GProp is competitive with fully supervised methods on the bandit task and achieves the best performance to date on the octopus arm.

Connected through hyperedge HE-1 (induced by source paper "from persistent homology to reinforcement learning with applications for retail banking"):
  with members:
    - "non negative paratuck2 tensor decomposition combined to lstm network for smart contracts profiling"
    - "user device authentication in mobile banking using aphen for paratuck2 tensor decomposition"
    - "phom gem persistent homology for generative models"
    - "predicting sparse clients actions with cpopt net in the banking environment"
    - "shampoo preconditioned stochastic tensor optimization"
    - "neural tensor factorization"
    - "implicit quantile networks for distributional reinforcement learning"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "augmented random search for quadcopter control an alternative to reinforcement learning"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "reinforcement learning for uav attitude control"
    - "learning data augmentation policies using augmented random search"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "guiding the search in continuous state action spaces by learning an action sampling distribution from off target samples"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "towards generalization and simplicity in continuous control"

Connected through hyperedge HE-3 (induced by source paper "mqlv modified q learning for vasicek model"):
  with members:
    - "implicit quantile networks for distributional reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "learning continuous control policies by stochastic value gradients"
    - "deep reinforcement learning with double q learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"
    - "pedestrian detection with unsupervised multi stage feature learning"

Connected through hyperedge HE-4 (induced by source paper "mqlv optimal policy of money management in retail banking with q learning"):
  with members:
    - "implicit quantile networks for distributional reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "learning continuous control policies by stochastic value gradients"
    - "deep reinforcement learning with double q learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"
    - "pedestrian detection with unsupervised multi stage feature learning"

Connected through hyperedge HE-5 (induced by source paper "actor critic versus direct policy search a comparison based on sample complexity"):
  with members:
    - "continuous deep q learning with model based acceleration"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning continuous control policies by stochastic value gradients"
    - "memory based control with recurrent neural networks"
    - "path integral policy improvement with covariance matrix adaptation"

Connected through hyperedge HE-6 (induced by source paper "semantics representations and grammars for deep learning"):
  with members:
    - "deep online convex optimization by putting forecaster to sleep"
    - "blocks and fuel frameworks for deep learning"
    - "deep learning in neural networks"
    - "fast convergence of regularized learning in games"
    - "continuous control with deep reinforcement learning"
    - "gradient estimation using stochastic computation graphs"
    - "train faster generalize better stability of stochastic gradient descent"
    - "difference target propagation"

Connected through hyperedge HE-7 (induced by source paper "memory based control with recurrent neural networks"):
  with members:
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "learning continuous control policies by stochastic value gradients"
    - "deep recurrent q learning for partially observable mdps"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-8 (induced by source paper "continuous control with deep reinforcement learning"):
  with members:
    - "gradient estimation using stochastic computation graphs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"
    - "learning continuous control policies by stochastic value gradients"
    - "memory based control with recurrent neural networks"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "playing atari with 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.LG(Machine Learning)