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: "How to Use Expert Advice (Extended Abstract)"
  Abstract: We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worst-case situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is generated. We measure the performance of the algorithm by the difference between the expected number of mistakes it makes on the bit sequence and the expected number of mistakes made by the best expert on this sequence, where the expectation is taken with respect to the randomization in the predictions. We show that the minimum achievable difference is on the order of the square root of the number of mistakes of the best expert, and we give efficient algorithms that achieve this. Our upper and lower bounds have matching leading constants in most cases. We give implications of this result on the performance of batch learning algorithms in a PAC setting which improve on the best results currently known in this context. We also extend our analysis to the case in which log loss is used instead of the expected number of mistakes.

Connected through hyperedge HE-1 (induced by source paper "Update rules for parameter estimation in Bayesian networks"):
  with members:
    - "A Comparison of New and Old Algorithms for A Mixture Estimation Problem"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"
    - "A Tutorial on Learning With Bayesian Networks"
    - "On-Line Portfolio Selection Using Multiplicative Updates"

Connected through hyperedge HE-2 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"):
  with members:
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "On Pruning and Averaging Decision Trees"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"
    - "Adaptive Mixtures of Probabilistic Transducers"
    - "Using and combining predictors that specialize"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"

Connected through hyperedge HE-3 (induced by source paper "Adaptive Mixtures of Probabilistic Transducers"):
  with members:
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"

Connected through hyperedge HE-4 (induced by source paper "Efficient Distribution-free Learning of Probabilistic Concepts"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"
    - "Toward Efficient Agnostic Learning"
    - "Learning Switching Concepts"

Connected through hyperedge HE-5 (induced by source paper "WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS"):
  with members:
    - "Predicting a binary sequence almost as well as the optimal biased coin"

Connected through hyperedge HE-6 (induced by source paper "ON-LINE LEARNING OF LINEAR FUNCTIONS"):
  with members:
    - "Worst-case Quadratic Loss Bounds for Prediction Using Linear Functions and Gradient Descent"
    - "Improved Bounds about On-line Learning of Smooth Functions of a Single Variable"

Connected through hyperedge HE-7 (induced by source paper "Using and combining predictors that specialize"):
  with members:
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "Context-sensitive learning methods for text categorization"
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"

Connected through hyperedge HE-8 (induced by source paper "Context-sensitive learning methods for text categorization"):
  with members:
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Learning Trees and Rules with Set-valued Features"
    - "Mistake-Driven Learning in Text Categorization"
    - "Using and combining predictors that specialize"
    - "Transferring and Retraining Learned Information Filters"

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