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: "gradient based reinforcement planning in policy search methods"
  Abstract: AbstractWe introduce a learning method called “gradient-based reinforcement planning” (GREP).Unlike traditional DP methods that improve their policy backwards in time, GREP is agradient-based method that plans ahead and improves its policy before it actually acts in theenvironment. We derive formulas for the exact policy gradient that maximizes the expectedfuture reward and conﬁrm our ideas with numerical experiments. 1 Introduction It has been shown that planning can dramatically improve convergence in reinforcement learning(RL) (?; ?). However, most RL methods that explicitly use planning that have been proposed arevalue (or Q-value) based methods, such as Dyna-Q or prioritized sweeping .Recently, much attention is directed to so-called policy-gradient methods that improve theirpolicy directly by calculating the derivative of the future expected reward with respect to thepolicy parameters. Gradient based methods are believed to be more advantageous than value-function based methods in huge state spaces and in POMDP settings. Probably the ﬁrst gradientbased RL formulation is class of REINFORCE algorithms ofWilliams (?). Other more recent methods are, e.g., (?; ?; ?). Our approach of deriving thegradient has the ﬂavor of (?) who derive the gradient using future state probabilities.Our novel contribution in this paper is to combine gradient-based learning with explicit plan-ning. We introduce “gradient-based reinforcement planning” (GREP) that improves a policy

Connected through hyperedge HE-1 (induced by source paper "universal algorithmic intelligence a mathematical top down approach"):
  with members:
    - "convergence and loss bounds for bayesian sequence prediction"
    - "the fastest and shortest algorithm for all well defined problems"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"
    - "optimal ordered problem solver"
    - "universal sequential decisions in unknown environments"
    - "general loss bounds for universal sequence prediction"
    - "convergence and error bounds for universal prediction of nonbinary sequences"
    - "market based reinforcement learning in partially observable worlds"

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)