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 dynamic bayesian networks"
  Abstract: Feature Markov Decision Processes (PhiMDPs) are well-suited for learning agents in general environments. Nevertheless, unstructured (Phi)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale real-world problems. In this article I extend PhiMDP to PhiDBN. The primary contribution is to derive a cost criterion that allows to automatically extract the most relevant features from the environment, leading to the "best" DBN representation. I discuss all building blocks required for a complete general learning algorithm.

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 "ethical artificial intelligence"):
  with members:
    - "logical limitations to machine ethics with consequences to lethal autonomous weapons"
    - "show and tell a neural image caption generator"
    - "playing atari with deep reinforcement learning"
    - "model based utility functions"
    - "feature reinforcement learning part i unstructured mdps"
    - "is there an elegant universal theory of prediction"

Connected through hyperedge HE-3 (induced by source paper "one decade of universal artificial intelligence"):
  with members:
    - "can intelligence explode"
    - "principles of solomonoff induction and aixi"
    - "asymptotically optimal agents"
    - "universal learning theory"
    - "an approximation of the universal intelligence measure"
    - "axioms for rational reinforcement learning"
    - "non equivalence of universal priors"
    - "feature reinforcement learning in practice"

Connected through hyperedge HE-4 (induced by source paper "model based utility functions"):
  with members:
    - "feature reinforcement learning part i unstructured mdps"

Connected through hyperedge HE-5 (induced by source paper "open problems in universal induction intelligence"):
  with members:
    - "policy iteration for factored mdps"
    - "online planning algorithms for pomdps"
    - "feature markov decision processes"
    - "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-6 (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"
    - "online planning algorithms for pomdps"
    - "feature markov decision processes"
    - "universal reinforcement learning"
    - "universal algorithmic intelligence a mathematical top down approach"

Connected through hyperedge HE-7 (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"
    - "online planning algorithms for pomdps"
    - "feature markov decision processes"
    - "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-8 (induced by source paper "feature markov decision processes"):
  with members:
    - "model based bayesian reinforcement learning in large structured domains"
    - "online planning algorithms for pomdps"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "universal intelligence a definition of machine intelligence"
    - "optimal ordered problem solver"

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)