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 "striving for simplicity and performance in off policy drl output normalization and non uniform sampling"):
  Abstract: We aim to develop off-policy DRL algorithms that not only exceed state-of-the-art performance but are also simple and minimalistic. For standard continuous control benchmarks, Soft Actor Critic (SAC), which employs entropy maximization, currently provides state-of-the-art performance. We first demonstrate that the entropy term in SAC addresses action saturation due to the bounded nature of the action spaces. With this insight, we propose a streamlined algorithm with a simple normalization scheme or with inverted gradients. We show that both approaches can match SAC's sample efficiency performance without the need of entropy maximization. We then propose a simple non-uniform sampling method for selecting transitions from the replay buffer during training. Extensive experimental results demonstrate that our proposed sampling scheme leads to state of the art sample efficiency on challenging continuous control tasks. We combine all of our findings into one simple algorithm, which we call Streamlined Off Policy with Emphasizing Recent Experience, for which we provide robust public-domain code.

Includes members:
  Member "diagnosing bottlenecks in deep q learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction")
  Member "benchmarking model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ready policy one world building through active learning")
  Member "remember and forget for experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards simplicity in deep reinforcement learning streamlined off policy learning")
  Member "supervised policy update for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "first order optimization in policy space for constrained deep reinforcement learning")
  Member "clipped action policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme")
  Member "marginal policy gradients a unified family of estimators for bounded action spaces with applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards simplicity in deep reinforcement learning streamlined off policy learning")
  Member "curiosity driven experience prioritization via density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automatic curriculum learning for deep rl a short survey")
  Member "safe exploration in continuous action spaces":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to walk in the real world with minimal human effort")

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)