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 "expert augmented actor critic for vizdoom and montezumas revenge"):
  Abstract: We propose an expert-augmented actor-critic algorithm, which we evaluate on two environments with sparse rewards: Montezumas Revenge and a demanding maze from the ViZDoom suite. In the case of Montezumas Revenge, an agent trained with our method achieves very good results consistently scoring above 27,000 points (in many experiments beating the first world). With an appropriate choice of hyperparameters, our algorithm surpasses the performance of the expert data. In a number of experiments, we have observed an unreported bug in Montezumas Revenge which allowed the agent to score more than 800,000 points.

Includes members:
  Member "pretraining deep actor critic reinforcement learning algorithms with expert demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning for nested polar code construction")
  Member "playing hard exploration games by watching youtube":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "td or not td analyzing the role of temporal differencing in deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural temporal difference learning converges to global optima")
  Member "observe and look further achieving consistent performance on atari":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "learning montezuma s revenge from a single demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic curriculum learning for deep rl a short survey")
  Member "hindsight experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "sample efficient actor critic reinforcement learning with supervised data for dialogue management":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical reinforcement learning for open domain dialog")

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)