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: "An Interactive Model of Teaching"
  Abstract: Previous teaching models in the learning theory community have been batch models. That is, in these models the teacher has generated a single set of helpful examples to present to the learner. In this paper we present an interactive model in which the learner has the ability to ask queries as in the query learning model of Angluin [1]. We show that this model is at least as powerful as previous teaching models. We also show that anything learnable with queries, even by a randomized learner, is teachable in our model. In all previous teaching models, all classes shown to be teachable are known to be efficiently learnable. An important concept class that is not known to be learnable is DNF formulas. We demonstrate the power of our approach by providing a deterministic teacher and learner for the class of DNF formulas. The learner makes only equivalence queries and all hypotheses are also DNF formulas.

Connected through hyperedge HE-1 (induced by source paper "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with Queries"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"

Connected through hyperedge HE-2 (induced by source paper "Learning Unions of Boxes with Membership and Equivalence Queries"):
  with members:
    - "Learning Boxes in High Dimension"
    - "Learning From a Consistently Ignorant Teacher"

Connected through hyperedge HE-3 (induced by source paper "The Power of Self-Directed Learning"):
  with members:
    - "Online Learning versus O*ine Learning"
    - "Teaching a Smarter Learner"

Connected through hyperedge HE-4 (induced by source paper "Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)"):
  with members:
    - "Simulating Access to Hidden Information while Learning"
    - "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with Queries"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"

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