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: "feature markov decision processes"
  Abstract: General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is an art performed by human designers to extract the right state representation out of the bare observations, i.e. to reduce the agent setup to the MDP framework. Before we can think of mechanizing this search for suitable MDPs, we need a formal objective criterion. The main contribution of this article is to develop such a criterion. I also integrate the various parts into one learning algorithm. Extensions to more realistic dynamic Bayesian networks are developed in a companion article.

Connected through hyperedge HE-1 (induced by source paper "extreme state aggregation beyond mdps"):
  with members:
    - "optimal regret bounds for selecting the state representation in reinforcement learning"
    - "the sample complexity of general reinforcement learning"
    - "selecting the state representation in reinforcement learning"
    - "metrics for finite markov decision processes"
    - "pac bounds for discounted mdps"
    - "feature reinforcement learning in practice"
    - "consistency of feature markov processes"
    - "feature reinforcement learning part i unstructured mdps"

Connected through hyperedge HE-2 (induced by source paper "open problems in universal induction intelligence"):
  with members:
    - "policy iteration for factored mdps"
    - "feature dynamic bayesian networks"
    - "online planning algorithms for pomdps"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "a collection of definitions of intelligence"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "tests of machine intelligence"
    - "algorithmic information theory a brief non technical guide to the field"

Connected through hyperedge HE-3 (induced by source paper "a monte carlo aixi approximation"):
  with members:
    - "temporal difference networks"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "reinforcement learning via aixi approximation"
    - "closing the learning planning loop with predictive state representations"
    - "feature dynamic bayesian networks"
    - "online planning algorithms for pomdps"
    - "universal reinforcement learning"
    - "universal algorithmic intelligence a mathematical top down approach"

Connected through hyperedge HE-4 (induced by source paper "feature reinforcement learning part i unstructured mdps"):
  with members:
    - "model based bayesian exploration"
    - "model based bayesian reinforcement learning in large structured domains"
    - "feature dynamic bayesian networks"
    - "online planning algorithms for pomdps"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "universal intelligence a definition of machine intelligence"
    - "universal learning of repeated matrix games"
    - "optimal ordered problem solver"

Connected through hyperedge HE-5 (induced by source paper "feature dynamic bayesian networks"):
  with members:
    - "policy iteration for factored mdps"
    - "online planning algorithms for pomdps"
    - "universal intelligence a definition of machine intelligence"
    - "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)