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 in parameterized action space"
  Abstract: Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, to the best of our knowledge no previous work has succeeded at using deep neural networks in structured (parameterized) continuous action spaces. To fill this gap, this paper focuses on learning within the domain of simulated RoboCup soccer, which features a small set of discrete action types, each of which is parameterized with continuous variables. The best learned agent can score goals more reliably than the 2012 RoboCup champion agent. As such, this paper represents a successful extension of deep reinforcement learning to the class of parameterized action space MDPs.

Connected through hyperedge HE-1 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "benchmarking model based reinforcement learning"
    - "remember and forget for experience replay"
    - "supervised policy update for deep reinforcement learning"
    - "clipped action policy gradient"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "curiosity driven experience prioritization via density estimation"
    - "safe exploration in continuous action spaces"

Connected through hyperedge HE-2 (induced by source paper "cluster based social reinforcement learning"):
  with members:
    - "message dropout an efficient training method for multi agent deep reinforcement learning"
    - "emergent coordination through competition"
    - "personalization of health interventions using cluster based reinforcement learning"
    - "negative update intervals in deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "m 3 rl mind aware multi agent management reinforcement learning"

Connected through hyperedge HE-3 (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-4 (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"
    - "reinforcement learning with parameterized actions"

Connected through hyperedge HE-5 (induced by source paper "human level performance in 3d multiplayer games with population based reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning with opponent learning awareness"
    - "feudal networks for hierarchical reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "emergence of grounded compositional language in multi agent populations"
    - "population based training of neural networks"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "towards simplicity in deep reinforcement learning streamlined off policy learning"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "benchmarking model based reinforcement learning"
    - "remember and forget for experience replay"
    - "supervised policy update for deep reinforcement learning"
    - "clipped action policy gradient"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "curiosity driven experience prioritization via density estimation"
    - "safe exploration in continuous action spaces"

Connected through hyperedge HE-7 (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-8 (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"

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)