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: "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
  Abstract: The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. In this work, we discover the interaction between sub-tasks from their resulting state-action trajectory sequences using a directed graphical model. We propose a new algorithm based on the generative adversarial imitation learning framework which automatically learns sub-task policies from unsegmented demonstrations. Our approach maximizes the directed information flow in the graphical model between sub-task latent variables and their generated trajectories. We also show how our approach connects with the existing Options framework, which is commonly used to learn hierarchical policies.

Connected through hyperedge HE-1 (induced by source paper "domain adversarial and conditional state space model for imitation learning"):
  with members:
    - "to follow or not to follow selective imitation learning from observations"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"
    - "learning belief representations for imitation learning in pomdps"
    - "learning robotic manipulation through visual planning and acting"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "recent advances in imitation learning from observation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"

Connected through hyperedge HE-2 (induced by source paper "oirl robust adversarial inverse reinforcement learning with temporally extended actions"):
  with members:
    - "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"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"
    - "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 "meta inverse reinforcement learning with probabilistic context variables"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "multi task maximum entropy inverse reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning to optimize neural nets"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"

Connected through hyperedge HE-4 (induced by source paper "sub policy adaptation for hierarchical reinforcement learning"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "self supervised learning of image embedding for continuous control"
    - "credit assignment techniques in stochastic computation graphs"
    - "hierarchical reinforcement learning with hindsight"
    - "hierarchical visuomotor control of humanoids"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-5 (induced by source paper "relay policy learning solving long horizon tasks via imitation and reinforcement learning"):
  with members:
    - "learning latent plans from play"
    - "temporal difference models model free deep rl for model based control"
    - "visual reinforcement learning with imagined goals"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "diversity is all you need learning skills without a reward function"
    - "hierarchical imitation and reinforcement learning"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "hindsight experience replay"

Connected through hyperedge HE-6 (induced by source paper "podnet a neural network for discovery of plannable options"):
  with members:
    - "inforl interpretable reinforcement learning using information maximization"
    - "efficiently combining human demonstrations and interventions for safe training of autonomous systems in real time"
    - "diversity is all you need learning skills without a reward function"
    - "variational option discovery algorithms"
    - "robust imitation of diverse behaviors"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "end to end learning for self driving cars"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-7 (induced by source paper "mature gail imitation learning for low level and high dimensional input using global encoder and cost transformation"):
  with members:
    - "world models"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "rainbow combining improvements in deep reinforcement learning"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "control of memory active perception and action in minecraft"
    - "trust region policy optimization"

Connected through hyperedge HE-8 (induced by source paper "grp model for sensorimotor learning"):
  with members:
    - "using deep reinforcement learning to learn high level policies on the atrias biped"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "sfv reinforcement learning of physical skills from videos"
    - "learning human behaviors from motion capture by adversarial imitation"

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)