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: "an information theoretic optimality principle for deep reinforcement learning"
  Abstract: We methodologically address the problem of Q-value overestimation in deep reinforcement learning to handle high-dimensional state spaces efficiently. By adapting concepts from information theory, we introduce an intrinsic penalty signal encouraging reduced Q-value estimates. The resultant algorithm encompasses a wide range of learning outcomes containing deep Q-networks as a special case. Different learning outcomes can be demonstrated by tuning a Lagrange multiplier accordingly. We furthermore propose a novel scheduling scheme for this Lagrange multiplier to ensure efficient and robust learning. In experiments on Atari, our algorithm outperforms other algorithms (e.g. deep and double deep Q-networks) in terms of both game-play performance and sample complexity. These results remain valid under the recently proposed dueling architecture.

Connected through hyperedge HE-1 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment"):
  with members:
    - "mutual information regularization in markov decision processes and actor critic learning"
    - "empowerment driven exploration using mutual information estimation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "a unified strategy for implementing curiosity and empowerment driven reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (induced by source paper "mutual information regularization in markov decision processes and actor critic learning"):
  with members:
    - "an information theoretic on line learning principle for specialization in hierarchical decision making systems"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "equivalence between policy gradients and soft q learning"
    - "bridging the gap between value and policy based reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "alpha alpha rank practically scaling alpha rank through stochastic optimisation"):
  with members:
    - "a generalized training approach for multiagent learning"
    - "multiagent evaluation under incomplete information"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "multi agent generalized recursive reasoning"
    - "openspiel a framework for reinforcement learning in games"
    - "bi level actor critic for multi agent coordination"
    - "open ended learning in symmetric zero sum games"

Connected through hyperedge HE-4 (induced by source paper "an information theoretic on line learning principle for specialization in hierarchical decision making systems"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "systems of bounded rational agents with information theoretic constraints"
    - "an information theoretic on line update principle for perception action coupling"
    - "bounded rational decision making with adaptive neural network priors"
    - "divide and conquer reinforcement learning"
    - "constrained policy optimization"
    - "asynchronous methods for deep reinforcement learning"
    - "planning with information processing constraints and model uncertainty in markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "bounded rational decision making from elementary computations that reduce uncertainty"):
  with members:
    - "systems of bounded rational agents with information theoretic constraints"
    - "bounded rational decision making with adaptive neural network priors"
    - "a short characterization of relative entropy"
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "information measures the curious case of the binary alphabet"
    - "a minimum relative entropy principle for learning and acting"
    - "provably bounded optimal agents"

Connected through hyperedge HE-6 (induced by source paper "multi task generative adversarial nets with shared memory for cross domain coordination control"):
  with members:
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "adversarial multi task learning for text classification"
    - "deep multi task representation learning a tensor factorisation approach"
    - "connecting generative adversarial networks and actor critic methods"
    - "deep generative image models using a laplacian pyramid of adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "balancing two player stochastic games with soft q learning"):
  with members:
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "planning with information processing constraints and model uncertainty in markov decision processes"
    - "trust region policy optimization"
    - "toward large scale agent guidance in an urban taxi service"

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)