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: "experiments with infinite horizon policy gradient estimation"
  Abstract: In this paper, we present algorithms that perform gradient ascent of the average reward in a partially observable Markov decision process (POMDP). These algorithms are based on GPOMDP, an algorithm introduced in a companion paper (Baxter & Bartlett, 2001), which computes biased estimates of the performance gradient in POMDPs. The algorithm's chief advantages are that it uses only one free parameter β ∈ [0, 1], which has a natural interpretation in terms of bias-variance trade-off, it requires no knowledge of the underlying state, and it can be applied to infinite state, control and observation spaces. We show how the gradient estimates produced by GPOMDP can be used to perform gradient ascent, both with a traditional stochastic-gradient algorithm, and with an algorithm based on conjugate-gradients that utilizes gradient information to bracket maxima in line searches. Experimental results are presented illustrating both the theoretical results of Baxter and Bartlett (2001) on a toy problem, and practical aspects of the algorithms on a number of more realistic problems.

Connected through hyperedge HE-1 (induced by source paper "optimal and low complexity dynamic spectrum access for rf powered ambient backscatter system with online reinforcement learning"):
  with members:
    - "reinforcement learning approach for rf powered cognitive radio network with ambient backscatter"
    - "stackelberg game for distributed time scheduling in rf powered backscatter cognitive radio networks"
    - "energy efficient scheduling for loss tolerant iot applications with uninformed transmitter"
    - "inference from randomized transmissions by many backscatter sensors"
    - "ambient backscatter communications a contemporary survey"
    - "mimo wiretap channel under receiver side power constraints with applications to wireless power transfer and cognitive radio"
    - "wirelessly powered backscatter communication networks modeling coverage and capacity"
    - "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis"

Connected through hyperedge HE-2 (induced by source paper "traffic light control using deep policy gradient and value function based reinforcement learning"):
  with members:
    - "deep reinforcement learning an overview"
    - "asynchronous methods for deep reinforcement learning"
    - "using a deep reinforcement learning agent for traffic signal control"
    - "gradient estimation using stochastic computation graphs"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "policy learning for time bounded reachability in continuous time markov decision processes via doubly stochastic gradient ascent"):
  with members:
    - "learning and designing stochastic processes from logical constraints"
    - "optimal continuous time markov decisions"
    - "smoothed model checking for uncertain continuous time markov chains"
    - "path integral policy improvement with covariance matrix adaptation"
    - "finite optimal control for time bounded reachability in ctmdps and continuous time markov games"

Connected through hyperedge HE-4 (induced by source paper "ares adaptive receding horizon synthesis of optimal plans"):
  with members:
    - "policy learning for time bounded reachability in continuous time markov decision processes via doubly stochastic gradient ascent"
    - "path integral policy improvement with covariance matrix adaptation"
    - "the convergence of bird flocking"
    - "v like formations in flocks of artificial birds"

Connected through hyperedge HE-5 (induced by source paper "bayesian reinforcement learning a survey"):
  with members:
    - "benchmarking for bayesian reinforcement learning"
    - "on the prior sensitivity of thompson sampling"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "a greedy approximation of bayesian reinforcement learning with probably optimistic transition model"
    - "model based bayesian exploration"
    - "further optimal regret bounds for thompson sampling"
    - "model based bayesian reinforcement learning in large structured domains"

Connected through hyperedge HE-6 (induced by source paper "decentralized delay optimal control for interference networks with limited renewable energy storage"):
  with members:
    - "solving pomdps by searching the space of finite policies"
    - "cooperative algorithms for mimo interference channels"
    - "delay optimal power and precoder adaptation for multi stream mimo systems"
    - "degrees of freedom of the k user m times n mimo interference channel"
    - "approaching the capacity of wireless networks through distributed interference alignment"
    - "competitive design of multiuser mimo systems based on game theory a unified view"

Connected through hyperedge HE-7 (induced by source paper "infinite horizon policy gradient estimation"):
  with members:
    - "learning to cooperate via policy search"
    - "learning finite state controllers for partially observable environments"

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)