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 reinforcement learning using spatio temporal abstractions and deep neural networks"
  Abstract: This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states. Identifying such structures present in the task provides ways to simplify and speed up reinforcement learning learning algorithms. These structures also help to generalize such algorithms over multiple tasks without relearning policies from scratch. We use ideas from dynamical systems to find metastable regions in the state space and associate them with abstract states. The spectral clustering algorithm PCCA+ is used to identify suitable abstractions aligned to the underlying structure. Skills are defined in terms of the transitions between such abstract states. The connectivity information from PCCA+ is used to generate these skills or options. The skills are independent of the learning task and can be efficiently reused across a variety of tasks defined over a common state space. Another major advantage of the approach is that it does not need a prior model of the MDP and can work well even when the MDPs are constructed from sampled trajectories. Finally, we present our attempts to extend the automated skills acquisition framework to complex tasks such as learning to play video games where we use deep learning techniques for representation learning to aid our spatio-temporal abstraction framework.

Connected through hyperedge HE-1 (induced by source paper "efficient exploration through intrinsic motivation learning for unsupervised subgoal discovery in model free hierarchical reinforcement learning"):
  with members:
    - "learning sparse representations in reinforcement learning"
    - "large scale study of curiosity driven learning"
    - "learning representations in model free hierarchical reinforcement learning"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-2 (induced by source paper "a compression inspired framework for macro discovery"):
  with members:
    - "a laplacian framework for option discovery in reinforcement learning"
    - "the option critic architecture"

Connected through hyperedge HE-3 (induced by source paper "learning representations in model free hierarchical reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "feudal networks for hierarchical reinforcement learning"
    - "multi level discovery of deep options"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "learning purposeful behaviour in the absence of rewards"
    - "the option critic architecture"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-4 (induced by source paper "the option critic architecture"):
  with members:
    - "adaptive skills adaptive partitions asap"
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "strategic attentive writer for learning macro actions"
    - "the arcade learning environment an evaluation platform for general agents"
    - "playing atari with deep reinforcement learning"
    - "compositional planning using optimal option models"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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)