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: "asymptotically optimal agents"
  Abstract: Artificial general intelligence aims to create agents capable of learning to solve arbitrary interesting problems. We define two versions of asymptotic optimality and prove that no agent can satisfy the strong version while in some cases, depending on discounting, there does exist a non-computable weak asymptotically optimal agent.

Connected through hyperedge HE-1 (induced by source paper "thompson sampling is asymptotically optimal in general environments"):
  with members:
    - "bad universal priors and notions of optimality"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a minimum relative entropy principle for learning and acting"
    - "universal intelligence a definition of machine intelligence"
    - "general discounting versus average reward"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

Connected through hyperedge HE-2 (induced by source paper "bad universal priors and notions of optimality"):
  with members:
    - "on the computability of aixi"
    - "optimistic agents are asymptotically optimal"
    - "an approximation of the universal intelligence measure"
    - "non equivalence of universal priors"
    - "open problems in universal induction intelligence"
    - "universal intelligence a definition of machine intelligence"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"
    - "a theory of universal artificial intelligence based on algorithmic complexity"

Connected through hyperedge HE-3 (induced by source paper "on the computability of solomonoff induction and knowledge seeking"):
  with members:
    - "bad universal priors and notions of optimality"
    - "on the computability of aixi"
    - "non equivalence of universal priors"
    - "universal prediction of selected bits"
    - "a philosophical treatise of universal induction"
    - "a theory of universal artificial intelligence based on algorithmic complexity"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-4 (induced by source paper "the sample complexity of general reinforcement learning"):
  with members:
    - "optimal regret bounds for selecting the state representation in reinforcement learning"
    - "on the sample complexity of reinforcement learning with a generative model"
    - "pac bounds for discounted mdps"
    - "optimistic agents are asymptotically optimal"
    - "time consistent discounting"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

Connected through hyperedge HE-5 (induced by source paper "one decade of universal artificial intelligence"):
  with members:
    - "can intelligence explode"
    - "principles of solomonoff induction and aixi"
    - "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"
    - "a philosophical treatise of universal induction"

Connected through hyperedge HE-6 (induced by source paper "optimistic agents are asymptotically optimal"):
  with members:
    - "selecting the state representation in reinforcement learning"
    - "pac bounds for discounted mdps"
    - "time consistent discounting"
    - "on the possibility of learning in reactive environments with arbitrary dependence"

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)