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 natural gradient policy search"
  Abstract: Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient policy update. We show that the natural gradient and trust region optimization are equivalent if we use the natural parameterization of a standard exponential policy distribution in combination with compatible value function approximation. Moreover, we show that standard natural gradient updates may reduce the entropy of the policy according to a wrong schedule leading to premature convergence. To control entropy reduction we introduce a new policy search method called compatible policy search (COPOS) which bounds entropy loss. The experimental results show that COPOS yields state-of-the-art results in challenging continuous control tasks and in discrete partially observable tasks.

Connected through hyperedge HE-1 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-2 (induced by source paper "smoothing policies and safe policy gradients"):
  with members:
    - "diverse exploration for fast and safe policy improvement"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "learning dexterous in hand manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "a lyapunov based approach to safe reinforcement learning"
    - "stochastic variance reduced policy gradient"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"

Connected through hyperedge HE-3 (induced by source paper "wasserstein robust reinforcement learning"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "non stationary markov decision processes a worst case approach using model based reinforcement learning"
    - "action robust reinforcement learning and applications in continuous control"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "beyond confidence regions tight bayesian ambiguity sets for robust mdps"
    - "deep lagrangian networks for end to end learning of energy based control for under actuated systems"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "lipschitz continuity in model based reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "compatible features for monotonic policy improvement"):
  with members:
    - "deep reinforcement learning that matters"
    - "constrained policy optimization"
    - "proximal policy optimization algorithms"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

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)