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: "a theory of universal artificial intelligence based on algorithmic complexity"
  Abstract: Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomono’s theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameterless theory of universal Artificial Intelligence. We give strong arguments that the resulting AI model is the most intelligent unbiased agent possible. We outline for a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning, how the AI model can formally solve them. The major drawback of the AI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AI tl , which is still eectively more intelligent than any other time t and space l bounded agent. The computation time of AI tl is of the order t·2 l . Other discussed topics are formal definitions of intelligence order relations, the horizon problem and relations of the AI theory to other AI approaches.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (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"
    - "asymptotically optimal agents"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "bad universal priors and notions of optimality"):
  with members:
    - "on the computability of aixi"
    - "optimistic agents are asymptotically optimal"
    - "asymptotically optimal agents"
    - "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"

Connected through hyperedge HE-4 (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"
    - "asymptotically optimal agents"
    - "non equivalence of universal priors"
    - "universal prediction of selected bits"
    - "a philosophical treatise of universal induction"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-5 (induced by source paper "on the computability of aixi"):
  with members:
    - "bad universal priors and notions of optimality"
    - "on the computability of solomonoff induction and knowledge seeking"
    - "non equivalence of universal priors"
    - "a philosophical treatise of universal induction"
    - "a monte carlo aixi approximation"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-6 (induced by source paper "algorithmic randomness as foundation of inductive reasoning and artificial intelligence"):
  with members:
    - "a complete theory of everything will be subjective"
    - "open problems in universal induction intelligence"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "algorithmic information theory a brief non technical guide to the field"
    - "universal intelligence a definition of machine intelligence"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (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"
    - "gradient based reinforcement planning in policy search methods"
    - "convergence and error bounds for universal prediction of nonbinary sequences"

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)