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: "Using and combining predictors that specialize"
  Abstract: We study online learning algorithms that predict by combining the predictions of several subordinate prediction algorithms, sometimes called experts. These simple algorithms belong to the multiplicative weights family of algorithms. The performance of these algorithms degrades only logarithmically with the number of experts, making them particularly useful in applications where the number of experts is very large. However, in applications such as text categorization, it is often natural for some of the experts to abstain from making predictions on some of the instances. We show how to transform algorithms that assume that all experts are always awake to algorithms that do not require this assumption. We also show how to derive corresponding loss bounds. Our method is very general, and can be applied to a large family of online learning algorithms. We also give applications to various prediction models including decision graphs and switching experts.

Connected through hyperedge HE-1 (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:
    - "How to Use Expert Advice (Extended Abstract)"
    - "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"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"

Connected through hyperedge HE-2 (induced by source paper "Learning Probabilistic Automata with Variable Memory Length"):
  with members:
    - "Learning Markov chains with variable memory length from noisy output"
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"
    - "Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation."
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

Connected through hyperedge HE-3 (induced by source paper "Context-sensitive learning methods for text categorization"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "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"
    - "Transferring and Retraining Learned Information Filters"

Connected through hyperedge HE-4 (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