human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "when to use parametric models in reinforcement learning"
  Abstract: We examine the question of when and how parametric models are most useful in reinforcement learning. In particular, we look at commonalities and differences between parametric models and experience replay. Replay-based learning algorithms share important traits with model-based approaches, including the ability to plan: to use more computation without additional data to improve predictions and behaviour. We discuss when to expect benefits from either approach, and interpret prior work in this context. We hypothesise that, under suitable conditions, replay-based algorithms should be competitive to or better than model-based algorithms if the model is used only to generate fictional transitions from observed states for an update rule that is otherwise model-free. We validated this hypothesis on Atari 2600 video games. The replay-based algorithm attained state-of-the-art data efficiency, improving over prior results with parametric models.

Connected through hyperedge HE-1 (induced by source paper "an optimistic perspective on offline reinforcement learning"):
  with members:
    - "keep doing what worked behavioral modelling priors for offline reinforcement learning"
    - "benchmarking batch deep reinforcement learning algorithms"
    - "off policy policy gradient with state distribution correction"
    - "behavior regularized offline reinforcement learning"
    - "challenges of real world reinforcement learning"
    - "model based reinforcement learning for atari"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "a framework for data driven robotics"

Connected through hyperedge HE-2 (induced by source paper "model based reinforcement learning for atari"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "benchmarking model based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "learning real world robot policies by dreaming"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "model based regularization for deep reinforcement learning with transcoder networks"
    - "model ensemble trust region policy optimization"

Connected through hyperedge HE-3 (induced by source paper "biologically inspired architectures for sample efficient deep reinforcement learning"):
  with members:
    - "random expert distillation imitation learning via expert policy support estimation"
    - "weight agnostic neural networks"
    - "scattering networks for hybrid representation learning"
    - "model based reinforcement learning for atari"
    - "kymatio scattering transforms in python"
    - "generative networks as inverse problems with scattering transforms"
    - "playing atari with six neurons"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "mastering atari go chess and shogi by planning with a learned model"):
  with members:
    - "off policy actor critic with shared experience replay"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "surprising negative results for generative adversarial tree search"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"

Question: Please tell me which class the center node 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)