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: "policy iteration for factored mdps"
  Abstract: Many large MDPs can be represented compactly using a dynamic Bayesian network. Although the structure of the value function does not retain the structure of the process, recent work has shown that value functions in factored MDPs can often be approximated well using a decomposed value function: a linear combination of restricted basis functions, each of which refers only to a small subset of variables. An approximate value function for a particular policy can be computed using approximate dynamic programming, but this approach (and others) can only produce an approximation relative to a distance metric which is weighted by the stationary distribution of the current policy. This type of weighted projection is ill-suited to policy improvement. We present a new approach to value determination, that uses a simple closed-form computation to directly compute a least-squares decomposed approximation to the value function for any weights. We then use this value determination algorithm as a subroutine in a policy iteration process. We show that, under reasonable restrictions, the policies induced by a factored value function are compactly represented, and can be manipulated efficiently in a policy iteration process. We also present a method for computing error bounds for decomposed value functions using a variable-elimination algorithm for function optimization. The complexity of all of our algorithms depends on the factorization of system dynamics and of the approximate value function.

Connected through hyperedge HE-1 (induced by source paper "multi agent thompson sampling for bandit applications with sparse neighbourhood structures"):
  with members:
    - "fleetwide data enabled reliability improvement of wind turbines"
    - "bayesian best arm identification for selecting influenza mitigation strategies"
    - "learning to optimize via posterior sampling"
    - "further optimal regret bounds for thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-2 (induced by source paper "recursive optimization of finite blocklength allocation to mitigate age of information outage"):
  with members:
    - "real time remote estimation with hybrid arq in wireless networked control"
    - "secure short packet communications for mission critical iot applications"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-3 (induced by source paper "finite blocklength allocation towards robust age of information optimization"):
  with members:
    - "real time remote estimation with hybrid arq in wireless networked control"
    - "secure short packet communications for mission critical iot applications"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "energy latency tradeoff in ultra reliable low latency communication with retransmissions"
    - "wirelessly powered communication networks with short packets"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-4 (induced by source paper "thompson sampling for factored multi agent bandits"):
  with members:
    - "fleetwide data enabled reliability improvement of wind turbines"
    - "learning to optimize via posterior sampling"
    - "further optimal regret bounds for thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "virel a variational inference framework for reinforcement learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "a theory of regularized markov decision processes"
    - "maven multi agent variational exploration"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "fourier policy gradients"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "dice the infinitely differentiable monte carlo estimator"

Connected through hyperedge HE-6 (induced by source paper "an incremental off policy search in a model free markov decision process using a single sample path"):
  with members:
    - "a cross entropy based stochastic approximation algorithm for reinforcement learning with linear function approximation"
    - "on convergence of emphatic temporal difference learning"
    - "infinite horizon policy gradient estimation"

Connected through hyperedge HE-7 (induced by source paper "a clustering approach to solving large stochastic matching problems"):
  with members:
    - "hierarchical solution of markov decision processes using macro actions"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-8 (induced by source paper "representation policy iteration"):
  with members:
    - "efficient solution algorithms for factored mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

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)