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: "reinforcement learning with parameterized actions"
  Abstract: We introduce a model-free algorithm for learning in Markov decision processes with parameterized actions-discrete actions with continuous parameters. At each step the agent must select both which action to use and which parameters to use with that action. We introduce the Q-PAMDP algorithm for learning in these domains, show that it converges to a local optimum, and compare it to direct policy search in the goal-scoring and Platform domains.

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 "smt based robot transition repair"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "robustfill neural program learning under noisy i o"
    - "human in the loop slam"
    - "a real time solver for time optimal control of omnidirectional robots with bounded acceleration"
    - "tortoise interactive system configuration repair"
    - "proximal policy optimization algorithms"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep reinforcement learning in parameterized action space"

Connected through hyperedge HE-3 (induced by source paper "efficient bimanual manipulation using learned task schemas"):
  with members:
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "active model learning and diverse action sampling for task and motion planning"
    - "learning to guide task and motion planning using score space representation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "meta learning shared hierarchies"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "hierarchical reinforcement learning method for autonomous vehicle behavior planning"):
  with members:
    - "analyzing knowledge transfer in deep q networks for autonomously handling multiple intersections"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning in parameterized action space"
    - "deep reinforcement learning with double q learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "visual analytics for team based invasion sports with significant events and markov reward process"):
  with members:
    - "classification of passes in football matches using spatiotemporal data"
    - "deep reinforcement learning in parameterized action space"
    - "deep recurrent q learning for partially observable mdps"

Connected through hyperedge HE-6 (induced by source paper "model learning for look ahead exploration in continuous control"):
  with members:
    - "world models"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-7 (induced by source paper "marginal policy gradients a unified family of estimators for bounded action spaces with applications"):
  with members:
    - "clipped action policy gradient"
    - "fourier policy gradients"
    - "self normalizing neural networks"
    - "starcraft ii a new challenge for reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "hierarchical approaches for reinforcement learning in parameterized action space"):
  with members:
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "trust region policy 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.AI(Artificial Intelligence)