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: "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"
  Abstract: A satisficing search problem consists of a set of probabilistic experiments to be performed in some order, without repetitions, until a satisfying configuration of successes and failures has been reached. The cost of performing the experiments depends on the order chosen. Earlier work has concentrated on finding optimal search strategies in special cases of this model, such as search trees and and-or graphs, when the cost function and the success probabilities for the experiments are given. In contrast, we study the complexity of "learning" an approximately optimal search strategy when some of the success probabilities are not known at the outset. Working in the fully general model, we show that if n is the number of unknown probabilities, and C is the maximum cost of performing all the experiments, then

Connected through hyperedge HE-1 (induced by source paper "Probably Approximately Optimal Derivation Strategies"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Learning Default Concepts"
    - "Learning an Optimally Accurate Representational System"

Connected through hyperedge HE-2 (induced by source paper "Learning Default Concepts"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Learning Active Classifiers"
    - "Probably Approximately Optimal Derivation Strategies"
    - "Knowing What Doesn't Matter: Exploiting Omitted Superfluous Data"

Connected through hyperedge HE-3 (induced by source paper "Learning Hierarchical Rule Sets"):
  with members:
    - "Learning rules with local exceptions"

Connected through hyperedge HE-4 (induced by source paper "A Statistical Approach to Solving the EBL Utility Problem"):
  with members:
    - "A Formal Framework for Speedup Learning from Problems and Solutions"
    - "Simple Selection of Utile Control Rules in Speedup Learning"
    - "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
    - "Learning Default Concepts"
    - "Learning an Optimally Accurate Representational System"
    - "Probably Approximately Optimal Derivation Strategies"

Connected through hyperedge HE-5 (induced by source paper "Learning an Optimally Accurate Representational System"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Probably Approximately Optimal Derivation Strategies"

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