human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "simple random search provides a competitive approach to reinforcement learning"):
  Abstract: A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions. We dispel such beliefs by introducing a random search method for training static, linear policies for continuous control problems, matching state-of-the-art sample efficiency on the benchmark MuJoCo locomotion tasks. Our method also finds a nearly optimal controller for a challenging instance of the Linear Quadratic Regulator, a classical problem in control theory, when the dynamics are not known. Computationally, our random search algorithm is at least 15 times more efficient than the fastest competing model-free methods on these benchmarks. We take advantage of this computational efficiency to evaluate the performance of our method over hundreds of random seeds and many different hyperparameter configurations for each benchmark task. Our simulations highlight a high variability in performance in these benchmark tasks, suggesting that commonly used estimations of sample efficiency do not adequately evaluate the performance of RL algorithms.

Includes members:
  Member "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "deep reinforcement learning that matters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "parameter space noise for exploration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "ray a distributed framework for emerging ai applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "scalable trust region method for deep reinforcement learning using kronecker factored approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "reproducibility of benchmarked deep reinforcement learning tasks for continuous control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling")
  Member "least squares temporal difference learning for the linear quadratic regulator":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems")
  Member "neural network dynamics for model based deep reinforcement learning with model free fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")

Question: Please tell me which class the source paper 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)