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: "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"
  Abstract: Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for intricate tasks. Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations. Yet in reality, the corpus of demonstrations may contain trajectories arising from a diverse set of underlying reward functions rather than a single one. Thus, in inverse reinforcement learning, it is useful to consider such a decomposition. The options framework in reinforcement learning is specifically designed to decompose policies in a similar light. We therefore extend the options framework and propose a method to simultaneously recover reward options in addition to policy options. We leverage adversarial methods to learn joint reward-policy options using only observed expert states. We show that this approach works well in both simple and complex continuous control tasks and shows significant performance increases in one-shot transfer learning.

Connected through hyperedge HE-1 (induced by source paper "joint goal and strategy inference across heterogeneous demonstrators via reward network distillation"):
  with members:
    - "distilling policy distillation"
    - "learning novel policies for tasks"
    - "ranking based reward extrapolation without rankings"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "repeated inverse reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "oirl robust adversarial inverse reinforcement learning with temporally extended actions"):
  with members:
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "robust imitation of diverse behaviors"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "learnings options end to end for continuous action tasks"
    - "proximal policy optimization algorithms"
    - "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (induced by source paper "multimodal trajectory optimization for motion planning"):
  with members:
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "learning novel policies for tasks"
    - "an algorithmic perspective on imitation learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "divide and conquer reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "recent advances in imitation learning from observation"):
  with members:
    - "hybrid reinforcement learning with expert state sequences"
    - "sample efficient adversarial imitation learning from observation"
    - "imitation learning from video by leveraging proprioception"
    - "3d human pose machines with self supervised learning"
    - "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"
    - "imitating latent policies from observation"
    - "internal model from observations for reward shaping"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-5 (induced by source paper "preventing imitation learning with adversarial policy ensembles"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adversarial policies attacking deep reinforcement learning"
    - "dynamics aware unsupervised discovery of skills"
    - "policy poisoning in batch reinforcement learning and control"
    - "learning novel policies for tasks"
    - "causal confusion in imitation learning"
    - "risk aware active inverse reinforcement learning"
    - "interpretable latent spaces for learning from demonstration"

Connected through hyperedge HE-6 (induced by source paper "generative adversarial imagination for sample efficient deep reinforcement learning"):
  with members:
    - "model based reinforcement learning for atari"
    - "surprising negative results for generative adversarial tree search"
    - "distributed distributional deterministic policy gradients"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "a distributional perspective on reinforcement learning"
    - "recurrent environment simulators"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-7 (induced by source paper "generalization in transfer learning"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "benchmark environments for multitask learning in continuous domains"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-8 (induced by source paper "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "inverse reinforcement learning for video games"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"
    - "playing hard exploration games by watching youtube"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "generative adversarial imitation from observation"
    - "one shot imitation from observing humans via domain adaptive meta learning"

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)