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: "parametrized deep q networks learning reinforcement learning with discrete continuous hybrid action space"
  Abstract: Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we consider the scenario with discrete-continuous hybrid action space. To handle hybrid action space, previous works either approximate the hybrid space by discretization, or relax it into a continuous set. In this paper, we propose a parametrized deep Q-network (P- DQN) framework for the hybrid action space without approximation or relaxation. Our algorithm combines the spirits of both DQN (dealing with discrete action space) and DDPG (dealing with continuous action space) by seamlessly integrating them. Empirical results on a simulation example, scoring a goal in simulated RoboCup soccer and the solo mode in game King of Glory (KOG) validate the efficiency and effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "continuous discrete reinforcement learning for hybrid control in robotics"):
  with members:
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "hybrid actor critic reinforcement learning in parameterized action space"
    - "dac the double actor critic architecture for learning options"
    - "multi pass q networks for deep reinforcement learning with parameterised action spaces"
    - "the termination critic"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "discrete and continuous action representation for practical rl in video games"):
  with members:
    - "distilling policy distillation"
    - "soft actor critic for discrete action settings"
    - "discretizing continuous action space for on policy optimization"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "managing engineering systems with large state and action spaces through deep reinforcement learning"
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-3 (induced by source paper "multi pass q networks for deep reinforcement learning with parameterised action spaces"):
  with members:
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "trust region policy optimization"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning in parameterized action space"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

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)