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: "ppo cma proximal policy optimization with covariance matrix adaptation"
  Abstract: Proximal Policy Optimization (PPO) is a highly popular model-free reinforcement learning (RL) approach. However, we observe that in a continuous action space, PPO can prematurely shrink the exploration variance, which leads to slow progress and may make the algorithm prone to getting stuck in local optima. Drawing inspiration from CMA-ES, a black-box evolutionary optimization method designed for robustness in similar situations, we propose PPO-CMA, a proximal policy optimization approach that adaptively expands the exploration variance to speed up progress. This can be considered as a form of action-space momentum. With only minor changes to PPO, our algorithm considerably improves performance in Roboschool continuous control benchmarks.

Connected through hyperedge HE-1 (induced by source paper "provably robust blackbox optimization for reinforcement learning"):
  with members:
    - "variance reduction for evolution strategies via structured control variates"
    - "introduction to online convex optimization"
    - "policies modulating trajectory generators"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "proximal policy optimization algorithms"
    - "sharper bounds for regression and low rank approximation with regularization"
    - "orthogonal random features"

Connected through hyperedge HE-2 (induced by source paper "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"):
  with members:
    - "variance reduction for evolution strategies via structured control variates"
    - "introduction to online convex optimization"
    - "policies modulating trajectory generators"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "proximal policy optimization algorithms"
    - "sharper bounds for regression and low rank approximation with regularization"
    - "orthogonal random features"

Connected through hyperedge HE-3 (induced by source paper "self imitation learning of locomotion movements through termination curriculum"):
  with members:
    - "learning symmetric and low energy locomotion"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sfv reinforcement learning of physical skills from videos"
    - "intelligent middle level game control"
    - "maximum a posteriori policy optimisation"
    - "proximal policy optimization algorithms"
    - "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)