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 "active deep q learning with demonstration"):
  Abstract: Recent research has shown that although Reinforcement Learning (RL) can benefit from expert demonstration, it usually takes considerable efforts to obtain enough demonstration. The efforts prevent training decent RL agents with expert demonstration in practice. In this work, we propose Active Reinforcement Learning with Demonstration (ARLD), a new framework to streamline RL in terms of demonstration efforts by allowing the RL agent to query for demonstration actively during training. Under the framework, we propose Active Deep Q-Network, a novel query strategy which adapts to the dynamically-changing distributions during the RL training process by estimating the uncertainty of recent states. The expert demonstration data within Active DQN are then utilized by optimizing supervised max-margin loss in addition to temporal difference loss within usual DQN training. We propose two methods of estimating the uncertainty based on two state-of-the-art DQN models, namely the divergence of bootstrapped DQN and the variance of noisy DQN. The empirical results validate that both methods not only learn faster than other passive expert demonstration methods with the same amount of demonstration and but also reach super-expert level of performance across four different tasks.

Includes members:
  Member "parameter space noise for exploration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "deeply aggrevated differentiable imitation learning for sequential prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control")
  Member "noisy networks for exploration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "deep q learning from demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "deep bayesian active learning with image data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "action for better prediction")
  Member "deep exploration via bootstrapped dqn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "playing atari games with deep reinforcement learning and human checkpoint replay":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey")

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)