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: "universal learning of repeated matrix games"
  Abstract: We study and compare the learning dynamics of two universal learning algorithms, one based on Bayesian learning and the other on prediction with expert advice. Both approaches have strong asymptotic performance guarantees. When confronted with the task of finding good long-term strategies in repeated 2x2 matrix games, they behave quite differently.

Connected through hyperedge HE-1 (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-2 (induced by source paper "feature reinforcement learning in practice"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "consistency of feature markov processes"
    - "feature reinforcement learning part i unstructured mdps"
    - "a monte carlo aixi approximation"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (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-4 (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"
    - "feature markov decision processes"
    - "universal reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "feature markov decision processes"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "universal intelligence a definition of machine intelligence"
    - "optimal ordered problem solver"

Connected through hyperedge HE-6 (induced by source paper "on the possibility of learning in reactive environments with arbitrary dependence"):
  with members:
    - "on sequence prediction for arbitrary measures"
    - "general discounting versus average reward"
    - "defensive universal learning with experts"
    - "prediction with expert advice by following the perturbed leader for general weights"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

Connected through hyperedge HE-7 (induced by source paper "asymptotic learnability of reinforcement problems with arbitrary dependence"):
  with members:
    - "defensive universal learning with experts"
    - "prediction with expert advice by following the perturbed leader for general weights"
    - "optimality of universal bayesian sequence prediction for general loss and alphabet"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

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.LG(Machine Learning)