human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Gambling in a rigged casino: The adversarial multi-armed bandit problem"
  Abstract: In the multi-armed bandit problem, a gambler must decide which arm of K non-identical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the trade-off between exploration (trying out each arm to find the best one) and exploitation (playing the arm believed to give the best payoff). Past solutions for the bandit problem have almost always relied on assumptions about the statistics of the slot machines. In this work, we make no statistical assumptions whatsoever about the nature of the process generating the payoffs of the slot machines. We give a solution to the bandit problem in which an adversary, rather than a well-behaved stochastic process, has complete control over the payoffs. In a sequence of T plays, we prove that the expected per-round payoff of our algorithm approaches that of the best arm at the rate O(T 1=2 ), and we give an improved rate of convergence when the best arm has fairly low payoff. We also prove a general matching lower bound on the best possible performance of any algorithm in our setting. In addition, we consider a setting in which the player has a team of experts advising him on which arm to play; here, we give a strategy that will guarantee expected payoff close to that of the best expert. Finally, we apply our result to the problem of learning to play an unknown repeated matrix game against an all-powerful adversary.

Connected through hyperedge HE-1 (induced by source paper "Dynamic Non-Bayesian Decision Making"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"):
  with members:
    - "Learning Sparse Perceptrons"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "An Efficient Boosting Algorithm for Combining Preferences"
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "Learning to Order Things"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Context-sensitive learning methods for text categorization"

Connected through hyperedge HE-3 (induced by source paper "How to Use Expert Advice (Extended Abstract)"):
  with members:
    - "Online Learning versus O*ine Learning"
    - "Simulating Access to Hidden Information while Learning"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Context-sensitive learning methods for text categorization"
    - "WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS"
    - "Large Margin Classification Using the Perceptron Algorithm"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"

Question: Please tell me which class the center node belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Theory