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: "defensive universal learning with experts"
  Abstract: This paper shows how universal learning can be achieved with expert advice. To this aim, we specify an experts algorithm with the following characteristics: (a) it uses only feedback from the actions actually chosen (bandit setup), (b) it can be applied with countably infinite expert classes, and (c) it copes with losses that may grow in time appropriately slowly. We prove loss bounds against an adaptive adversary. From this, we obtain a master algorithm for "reactive" experts problems, which means that the master's actions may influence the behavior of the adversary. Our algorithm can significantly outperform standard experts algorithms on such problems. Finally, we combine it with a universal expert class. The resulting universal learner performs -- in a certain sense -- almost as well as any computable strategy, for any online decision problem. We also specify the (worst-case) convergence speed, which is very slow.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "universal learning of repeated matrix games"
    - "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-4 (induced by source paper "a minimum relative entropy principle for learning and acting"):
  with members:
    - "model based bayesian exploration"
    - "a bayesian rule for adaptive control based on causal interventions"
    - "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-5 (induced by source paper "asymptotic learnability of reinforcement problems with arbitrary dependence"):
  with members:
    - "universal learning of repeated matrix games"
    - "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-6 (induced by source paper "universal learning of repeated matrix games"):
  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"
    - "goedel machines self referential universal problem solvers making provably optimal self improvements"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"
    - "optimal ordered problem solver"

Connected through hyperedge HE-7 (induced by source paper "fpl analysis for adaptive bandits"):
  with members:
    - "adaptive online prediction by following the perturbed leader"

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)