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: "The Power of Self-Directed Learning"
  Abstract: This paper studies self-directed learning, a variant of the on-line learning model in which the learner selects the presentation order for the instances. We give tight bounds on the complexity of self-directed learning for the concept classes of monomials, k-term DNF formulas, and orthogonal rectangles in f0; 1; ; n1g d . These results demonstrate that the number of mistakes under self-directed learning can be surprisingly small. We then prove that the model of self-directed learning is more powerful than all other commonly used on-line and query learning models. Next we explore the relationship between the complexity of self-directed learning and the Vapnik-Chervonenkis dimension. Finally, we explore a relationship between Mitchell's version space algorithm and the existence of self-directed learning algorithms that make few mistakes. fl Supported in part by a GE Foundation Junior Faculty Grant and NSF Grant CCR-9110108. Part of this research was conducted while the author was at the M.I.T. Laboratory for Computer Science and supported by NSF grant DCR-8607494 and a grant from the Siemens Corporation. Net address: sg@cs.wustl.edu.

Connected through hyperedge HE-1 (induced by source paper "An Interactive Model of Teaching"):
  with members:
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)"
    - "Learning Unions of Boxes with Membership and Equivalence Queries"

Connected through hyperedge HE-2 (induced by source paper "Online Learning versus O*ine Learning"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Apple Tasting and Nearly One-Sided Learning"
    - "The Weighted Majority Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Teaching a Smarter Learner"):
  with members:
    - "On the Sample Complexity of Weakly Learning"
    - "Learning Conjunctions of Horn Clauses"

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