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: "fpl analysis for adaptive bandits"
  Abstract: A main problem of "Follow the Perturbed Leader" strategies for online decision problems is that regret bounds are typically proven against oblivious adversary. In partial observation cases, it was not clear how to obtain performance guarantees against adaptive adversary, without worsening the bounds. We propose a conceptually simple argument to resolve this problem. Using this, a regret bound of O(t^(2/3)) for FPL in the adversarial multi-armed bandit problem is shown. This bound holds for the common FPL variant using only the observations from designated exploration rounds. Using all observations allows for the stronger bound of O(t^(1/2)), matching the best bound known so far (and essentially the known lower bound) for adversarial bandits. Surprisingly, this variant does not even need explicit exploration, it is self-stabilizing. However the sampling probabilities have to be either externally provided or approximated to sufficient accuracy, using O(t^2 log t) samples in each step.

Connected through hyperedge HE-1 (induced by source paper "beyond the hazard rate more perturbation algorithms for adversarial multi armed bandits"):
  with members:
    - "online linear optimization via smoothing"
    - "prediction by random walk perturbation"
    - "an efficient algorithm for learning with semi bandit feedback"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-2 (induced by source paper "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"):
  with members:
    - "online linear optimization via smoothing"
    - "prediction by random walk perturbation"
    - "an efficient algorithm for learning with semi bandit feedback"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "regret in online combinatorial optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "the on line shortest path problem under partial monitoring"

Connected through hyperedge HE-3 (induced by source paper "first order regret bounds for combinatorial semi bandits"):
  with members:
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "online linear optimization via smoothing"
    - "prediction by random walk perturbation"
    - "an efficient algorithm for learning with semi bandit feedback"
    - "regret in online combinatorial optimization"
    - "the on line shortest path problem under partial monitoring"
    - "prediction with expert advice by following the perturbed leader for general weights"

Connected through hyperedge HE-4 (induced by source paper "an efficient algorithm for learning with semi bandit feedback"):
  with members:
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "regret in online combinatorial optimization"
    - "the on line shortest path problem under partial monitoring"

Connected through hyperedge HE-5 (induced by source paper "prediction by random walk perturbation"):
  with members:
    - "regret in online combinatorial optimization"
    - "prediction with expert advice by following the perturbed leader for general weights"

Connected through hyperedge HE-6 (induced by source paper "universal learning of repeated matrix games"):
  with members:
    - "defensive universal learning with experts"
    - "adaptive online prediction by following the perturbed leader"
    - "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 "defensive universal learning with experts"):
  with members:
    - "adaptive online prediction by following the perturbed leader"
    - "prediction with expert advice by following the perturbed leader for general weights"
    - "towards a universal theory of artificial intelligence based on algorithmic probability and sequential decision theory"

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)