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 "regularized hierarchical policies for compositional transfer in robotics"):
  Abstract: The successful application of flexible, general learning algorithms -- such as deep reinforcement learning -- to real-world robotics applications is often limited by their poor data-efficiency. Domains with more than a single dominant task of interest encourage algorithms that share partial solutions across tasks to limit the required experiment time. We develop and investigate simple hierarchical inductive biases -- in the form of structured policies -- as a mechanism for knowledge transfer across tasks in reinforcement learning (RL). To leverage the power of these structured policies we design an RL algorithm that enables stable and fast learning. We demonstrate the success of our method both in simulated robot environments (using locomotion and manipulation domains) as well as real robot experiments, demonstrating substantially better data-efficiency than competitive baselines.

Includes members:
  Member "tf replicator distributed machine learning for researchers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial video generation on complex datasets")
  Member "exploiting hierarchy for learning and transfer in kl regularized rl":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "keep doing what worked behavioral modelling priors for offline reinforcement learning")
  Member "multitask soft option learning":
    also connected through other hyperedges:
      (none)
  Member "dac the double actor critic architecture for learning options":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "continuous discrete reinforcement learning for hybrid control in robotics")
  Member "information asymmetry in kl regularized rl":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "periodic intra ensemble knowledge distillation for reinforcement learning")
  Member "the termination critic":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "continuous discrete reinforcement learning for hybrid control in robotics")
  Member "deepmind control suite":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "impala scalable distributed deep rl with importance weighted actor learner architectures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")

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)