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: "Learning From a Population of Hypotheses"
  Abstract: We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the question of how to make optimal use of multiple independent runs of a mediocre learning algorithm, as well as settings in which the many hypotheses are obtained by a distributed population of identical learning agents.

Connected through hyperedge HE-1 (induced by source paper "Boosting a weak learning algorithm by majority To be published in Information and Computation"):
  with members:
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Sifting informative examples from a random source."
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Game Theory, On-line Prediction and Boosting"
    - "On the Sample Complexity of Weakly Learning"
    - "A Quantum Computational Learning Algorithm"

Connected through hyperedge HE-2 (induced by source paper "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Exactly Learning Automata with Small Cover Time"
    - "The Power of a Pebble: Exploring and Mapping Directed Graphs"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

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