human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Learning Hierarchical Rule Sets"):
  Abstract: We present an algorithm for learning sets of rules that are organized into up to k levels. Each level can contain an arbitrary number of rules "if c then l" where l is the class associated to the level and c is a concept from a given class of basic concepts. The rules of higher levels have precedence over the rules of lower levels and can be used to represent exceptions. As basic concepts we can use Boolean attributes in the infinite attribute space model, or certain concepts defined in terms of substrings. Given a sample of m examples, the algorithm runs in polynomial time and produces a consistent concept representation of size O((log m) k n k ), where n is the size of the smallest consistent representation with k levels of rules. This implies that the algorithm learns in the PAC model. The algorithm repeatedly applies the greedy heuristics for weighted set cover. The weights are obtained from approximate solutions to previous set cover problems.

Includes members:
  Member "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Probably Approximately Optimal Derivation Strategies")
  Member "Learning rules with local exceptions":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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