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: "towards a universal theory of artificial intelligence based on algorithmic probability and sequential decision theory"
  Abstract: Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown distribution. We unify both theories and give strong arguments that the resulting universal AIXI model behaves optimal in any computable environment. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI^tl, which is still superior to any other time t and space l bounded agent. The computation time of AIXI^tl is of the order t x 2^l.

Connected through hyperedge HE-1 (induced by source paper "an approximation of the universal intelligence measure"):
  with members:
    - "measuring intelligence through games"
    - "reinforcement learning via aixi approximation"
    - "a monte carlo aixi approximation"
    - "temporal difference updating without a learning rate"
    - "universal intelligence a definition of machine intelligence"

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"
    - "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-3 (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"

Connected through hyperedge HE-4 (induced by source paper "universal intelligence a definition of machine intelligence"):
  with members:
    - "a collection of definitions of intelligence"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "a formal measure of machine intelligence"
    - "general discounting versus average reward"
    - "universal sequential decisions in unknown environments"

Connected through hyperedge HE-5 (induced by source paper "defensive universal learning with experts"):
  with members:
    - "adaptive online prediction by following the perturbed leader"
    - "fpl analysis for adaptive bandits"
    - "prediction with expert advice by following the perturbed leader for general weights"

Connected through hyperedge HE-6 (induced by source paper "optimality of universal bayesian sequence prediction for general loss and alphabet"):
  with members:
    - "sequence prediction based on monotone complexity"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"
    - "convergence and error bounds for universal prediction of nonbinary sequences"
    - "minimum description length induction bayesianism and kolmogorov complexity"
    - "new error bounds for solomonoff prediction"

Connected through hyperedge HE-7 (induced by source paper "optimal ordered problem solver"):
  with members:
    - "the new ai general sound relevant for physics"
    - "goedel machines self referential universal problem solvers making provably optimal self improvements"
    - "the fastest and shortest algorithm for all well defined problems"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"
    - "market based reinforcement learning in partially observable worlds"
    - "reinforcement learning a survey"

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)