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: "regularised deep reinforcement learning with guaranteed convergence"
  Abstract: Deep Q-networks (DQNs) suffer from two important challenges hindering their application in real-world scenarios. First, DQNs overestimate Q-values which leads to increased sample complexity, and second, no theoretical convergence guarantees have been established. In this paper, we address both problems by introducing an intrinsic penalty signal arising from a Kullback-Leibler (KL) constraint that encourages reduced Q-value estimates. We then prove, for the first time, convergence to a stationary point under a specific scheduling of the penalisation magnitude. Our proofs operate in the deep reinforcement learning setting that considers convolutional and dense layers for Q-function approximation. Furthermore, we prove divergence of standard DQNs using a counter example that relates to the non-optimal choice of the history-scheduling parameter adopted by `vanilla' DQNs. We believe this can shed the light on some of the difficulties reported by researchers and practitioners in the field.

Connected through hyperedge HE-1 (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"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "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-2 (induced by source paper "multi task generative adversarial nets with shared memory for cross domain coordination control"):
  with members:
    - "adversarial multi task learning for text classification"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "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-3 (induced by source paper "bounded rational decision making with adaptive neural network priors"):
  with members:
    - "an information theoretic on line update principle for perception action coupling"
    - "divide and conquer reinforcement learning"
    - "planning with information processing constraints and model uncertainty in markov decision processes"
    - "neural adaptive sequential monte carlo"
    - "reversible jump mcmc simulated annealing for neural networks"

Connected through hyperedge HE-4 (induced by source paper "balancing two player stochastic games with soft q learning"):
  with members:
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "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)