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: "dynamic frame skip deep q network"
  Abstract: Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain. One of the important parameters in the Arcade Learning Environment (ALE) is the frame skip rate. It decides the granularity at which agents can control game play. A frame skip value of $k$ allows the agent to repeat a selected action $k$ number of times. The current state of the art architectures like Deep Q-Network (DQN) and Dueling Network Architectures (DuDQN) consist of a framework with a static frame skip rate, where the action output from the network is repeated for a fixed number of frames regardless of the current state. In this paper, we propose a new architecture, Dynamic Frame skip Deep Q-Network (DFDQN) which makes the frame skip rate a dynamic learnable parameter. This allows us to choose the number of times an action is to be repeated based on the current state. We show empirically that such a setting improves the performance on relatively harder games like Seaquest.

Connected through hyperedge HE-1 (induced by source paper "monte carlo tree search with scalable simulation periods for continuously running tasks"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "the arcade learning environment an evaluation platform for general agents"
    - "x armed bandits"

Connected through hyperedge HE-2 (induced by source paper "the atari grand challenge dataset"):
  with members:
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "deep q learning from demonstrations"
    - "generative adversarial imitation learning"
    - "a deep learning approach for joint video frame and reward prediction in atari games"
    - "asynchronous methods for deep reinforcement learning"
    - "playing atari games with deep reinforcement learning and human checkpoint replay"
    - "trust region policy optimization"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-3 (induced by source paper "learning to repeat fine grained action repetition for deep reinforcement learning"):
  with members:
    - "learning to translate in real time with neural machine translation"
    - "strategic attentive writer for learning macro actions"
    - "deep reinforcement learning with 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"

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)