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 rules with local exceptions"
  Abstract: We present a learning algorithm for rule-based concept representations called ripple-down rule sets. Ripple-down rule sets allow us to deal with the exceptions for each rule separately by introducing exception rules, exception rules for each exception rule etc. up to a constant depth. These local exception rules are in contrast to decision lists, in which the exception rules must be placed into a global ordering of the rules. The localization of exceptions makes it possible to represent concepts that have no decision list representation. On the other hand, decision lists with a constant number of alternations between rules for different classes can be represented by constant depth ripple-down rule sets with only a polynomial increase in size. Our algorithm is an Occam algorithm for constant depth ripple-down rule sets and, hence, a PAC learning algorithm. It is based on repeatedly applying the greedy approximation method for the weighted set cover problem to find good exception rule sets.

Connected through hyperedge HE-1 (induced by source paper "Learning Hierarchical Rule Sets"):
  with members:
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

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