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 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.

Connected through hyperedge HE-1 (induced by source paper "Learning rules with local exceptions"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Learning Default Concepts"
    - "Learning an Optimally Accurate Representational System"
    - "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