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: "learning abstract options"
  Abstract: Building systems that autonomously create temporal abstractions from data is a key challenge in scaling learning and planning in reinforcement learning. One popular approach for addressing this challenge is the options framework (Sutton et al., 1999). However, only recently in (Bacon et al., 2017) was a policy gradient theorem derived for online learning of general purpose options in an end to end fashion. In this work, we extend previous work on this topic that only focuses on learning a two-level hierarchy including options and primitive actions to enable learning simultaneously at multiple resolutions in time. We achieve this by considering an arbitrarily deep hierarchy of options where high level temporally extended options are composed of lower level options with finer resolutions in time. We extend results from (Bacon et al., 2017) and derive policy gradient theorems for a deep hierarchy of options. Our proposed hierarchical option-critic architecture is capable of learning internal policies, termination conditions, and hierarchical compositions over options without the need for any intrinsic rewards or subgoals. Our empirical results in both discrete and continuous environments demonstrate the efficiency 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 to teach in cooperative multiagent reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "hierarchical imitation and reinforcement learning"
    - "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 "parameter sharing in coagent networks"):
  with members:
    - "on the role of weight sharing during deep option learning"
    - "reinforcement learning without backpropagation or a clock"
    - "the option critic architecture"

Connected through hyperedge HE-3 (induced by source paper "routing networks and the challenges of modular and compositional computation"):
  with members:
    - "a meta mdp approach to exploration for lifelong reinforcement learning"
    - "reinforcement learning without backpropagation or a clock"
    - "the termination critic"
    - "modular networks learning to decompose neural computation"
    - "evolutionary architecture search for deep multitask networks"
    - "modular meta learning"
    - "diversity is all you need learning skills without a reward function"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"

Connected through hyperedge HE-4 (induced by source paper "self organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks"):
  with members:
    - "model based reinforcement learning for atari"
    - "a novel predictive coding inspired variational rnn model for online prediction and recognition"
    - "bayesian model agnostic meta learning"
    - "one big net for everything"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "probabilistic model agnostic meta learning"
    - "recurrent world models facilitate policy evolution"
    - "learning to embed probabilistic structures between deterministic chaos and random process in a variational bayes predictive coding rnn"

Connected through hyperedge HE-5 (induced by source paper "on the role of weight sharing during deep option learning"):
  with members:
    - "routing networks and the challenges of modular and compositional computation"
    - "reinforcement learning without backpropagation or a clock"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "progressive neural networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "scalable recollections for continual lifelong learning"

Connected through hyperedge HE-6 (induced by source paper "dac the double actor critic architecture for learning options"):
  with members:
    - "generalized off policy actor critic"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "ace an actor ensemble algorithm for continuous control with tree search"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"
    - "efficient parallel methods for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning from trajectories via subgoal discovery"):
  with members:
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "trajectory based learning for ball in maze games"
    - "investigating human priors for playing video games"
    - "fast policy learning through imitation and reinforcement"
    - "behavioral cloning from observation"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-8 (induced by source paper "learning hierarchical teaching in cooperative multiagent reinforcement learning"):
  with members:
    - "addressing function approximation error in actor critic methods"
    - "hierarchical imitation and reinforcement learning"
    - "learning to explore with meta policy gradient"
    - "automated curriculum learning for neural networks"
    - "counterfactual multi agent policy gradients"
    - "feudal networks for hierarchical reinforcement learning"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "multi agent actor critic for mixed cooperative competitive environments"

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)