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: "hierarchical imitation and reinforcement learning"
  Abstract: We study the problem of learning policies over long time horizons. We present a framework that leverages and integrates two key concepts. First, we utilize hierarchical policy classes that enable planning over different time scales, i.e., the high level planner proposes a sequence of subgoals for the low level planner to achieve. Second, we utilize expert demonstrations within the hierarchical action space to dramatically reduce cost of exploration. Our framework is flexible and can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels of the hierarchy. Using long-horizon benchmarks, including Montezuma's Revenge, we empirically demonstrate that our approach can learn significantly faster compared to hierarchical RL, and can be significantly more label- and sample-efficient compared to flat IL. We also provide theoretical analysis of the labeling cost for certain instantiations of our framework.

Connected through hyperedge HE-1 (induced by source paper "learning hierarchical teaching policies for cooperative agents"):
  with members:
    - "teaching on a budget in multi agent deep reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "learning abstract options"
    - "learning to teach in cooperative multiagent reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "automated curriculum learning for neural networks"
    - "counterfactual multi agent policy gradients"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning self correctable policies and value functions from demonstrations with negative sampling"):
  with members:
    - "generative predecessor models for sample efficient imitation learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "an algorithmic perspective on imitation learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "dual policy iteration"
    - "model ensemble trust region policy optimization"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-3 (induced by source paper "exploring applications of deep reinforcement learning for real world autonomous driving systems"):
  with members:
    - "on first order meta learning algorithms"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "learning to drive in a day"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "learning to combine primitive skills a step towards versatile robotic manipulation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "latent space policies for hierarchical reinforcement learning"
    - "neural modular control for embodied question answering"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-5 (induced by source paper "on multi agent learning in team sports games"):
  with members:
    - "winning isn t everything training human like agents for playtesting and game ai"
    - "skynet a top deep rl agent in the inaugural pommerman team competition"
    - "safer deep rl with shallow mcts a case study in pommerman"
    - "learning latent plans from play"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent adversarial inverse reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-6 (induced by source paper "hindsight trust region policy optimization"):
  with members:
    - "hierarchical reinforcement learning with hindsight"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "visual reinforcement learning with imagined goals"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "many goals reinforcement learning"
    - "hindsight experience replay"

Connected through hyperedge HE-7 (induced by source paper "watch try learn meta learning from demonstrations and reward"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "one shot high fidelity imitation training large scale deep nets with rl"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "task embedded control networks for few shot imitation learning"

Connected through hyperedge HE-8 (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"

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)