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: "deep reinforcement learning with macro actions"
  Abstract: Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the Atari domain. In this paper, we explore output representation modeling in the form of temporal abstraction to improve convergence and reliability of deep reinforcement learning approaches. We concentrate on macro-actions, and evaluate these on different Atari 2600 games, where we show that they yield significant improvements in learning speed. Additionally, we show that they can even achieve better scores than DQN. We offer analysis and explanation for both convergence and final results, revealing a problem deep RL approaches have with sparse reward signals.

Connected through hyperedge HE-1 (induced by source paper "construction of macro actions for deep reinforcement learning"):
  with members:
    - "macro action reinforcement learning with sequence disentanglement using variational autoencoder"
    - "meta learning shared hierarchies"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning and transfer of modulated locomotor controllers"

Connected through hyperedge HE-2 (induced by source paper "macro action reinforcement learning with sequence disentanglement using variational autoencoder"):
  with members:
    - "time series segmentation through automatic feature learning"
    - "learning to walk via deep reinforcement learning"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "strategic attentive writer for learning macro actions"
    - "deep reinforcement learning in parameterized action space"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-3 (induced by source paper "a deep hierarchical reinforcement learning algorithm in partially observable markov decision processes"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "multi level discovery of deep options"
    - "micro objective learning accelerating deep reinforcement learning through the discovery of continuous subgoals"
    - "the option critic architecture"
    - "classifying options for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "continuous deep q learning with model based acceleration"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-4 (induced by source paper "learning to repeat fine grained action repetition for deep reinforcement learning"):
  with members:
    - "dynamic frame skip deep q network"
    - "learning to translate in real time with neural machine translation"
    - "strategic attentive writer for learning macro actions"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-5 (induced by source paper "two dimensional anti jamming mobile communication based on reinforcement learning"):
  with members:
    - "jamming resistant learning in wireless networks"

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)