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: "adversarial imitation via variational inverse reinforcement learning"
  Abstract: We consider a problem of learning the reward and policy from expert examples under unknown dynamics. Our proposed method builds on the framework of generative adversarial networks and introduces the empowerment-regularized maximum-entropy inverse reinforcement learning to learn near-optimal rewards and policies. Empowerment-based regularization prevents the policy from overfitting to expert demonstrations, which advantageously leads to more generalized behaviors that result in learning near-optimal rewards. Our method simultaneously learns empowerment through variational information maximization along with the reward and policy under the adversarial learning formulation. We evaluate our approach on various high-dimensional complex control tasks. We also test our learned rewards in challenging transfer learning problems where training and testing environments are made to be different from each other in terms of dynamics or structure. The results show that our proposed method not only learns near-optimal rewards and policies that are matching expert behavior but also performs significantly better than state-of-the-art inverse reinforcement learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "efficiently guiding imitation learning algorithms with human gaze"):
  with members:
    - "gaze training by modulated dropout improves imitation learning"
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "understanding teacher gaze patterns for robot learning"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "inverse reinforcement learning for video games"
    - "agil learning attention from human for visuomotor tasks"
    - "reward learning from human preferences and demonstrations in atari"

Connected through hyperedge HE-2 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners"):
  with members:
    - "neural path planning fixed time near optimal path generation via oracle imitation"
    - "composing task agnostic policies with deep reinforcement learning"
    - "composing ensembles of policies with deep reinforcement learning"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "experience replay for continual learning"
    - "motion planning networks"

Connected through hyperedge HE-3 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment"):
  with members:
    - "mutual information regularization in markov decision processes and actor critic learning"
    - "empowerment driven exploration using mutual information estimation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "a unified strategy for implementing curiosity and empowerment driven reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-4 (induced by source paper "hindsight generative adversarial imitation learning"):
  with members:
    - "generative adversarial self imitation learning"
    - "zero shot visual imitation"
    - "curiosity driven experience prioritization via density estimation"
    - "self imitation learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "generative adversarial imitation from observation"
    - "energy based hindsight experience prioritization"

Connected through hyperedge HE-5 (induced by source paper "imitation learning as f divergence minimization"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "sfv reinforcement learning of physical skills from videos"
    - "generative adversarial imitation from observation"
    - "sample efficient imitation learning via generative adversarial nets"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "fast policy learning through imitation and reinforcement"
    - "behavioral cloning from observation"

Connected through hyperedge HE-6 (induced by source paper "state alignment based imitation learning"):
  with members:
    - "wasserstein adversarial imitation learning"
    - "provably efficient imitation learning from observation alone"
    - "efficient exploration via state marginal matching"
    - "imitation learning as f divergence minimization"
    - "imitating latent policies from observation"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-7 (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"
    - "generative adversarial imitation from observation"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"

Connected through hyperedge HE-8 (induced by source paper "imitation learning from video by leveraging proprioception"):
  with members:
    - "recent advances in imitation learning from observation"
    - "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"
    - "imitating latent policies from observation"
    - "an algorithmic perspective on imitation learning"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"
    - "generative adversarial imitation from observation"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "behavioral cloning from observation"

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)