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: "Teaching a Smarter Learner"
  Abstract: We introduce a formal model of teaching in which the teacher is tailored to a particular learner, yet the teaching protocol is designed so that no collusion is possible. Not surprisingly, such a model remedies the non-intuitive aspects of other models in which the teacher must successfully teach any consistent learner. We prove that any class that can be exactly identified by a deterministic polynomial-time algorithm with access to a very rich set of example-based queries is teachable by a computationally unbounded teacher and a polynomial-time learner. In addition, we present other general results relating this model of teaching to various previous results. We also consider the problem of designing teacher/learner pairs in which both the teacher and learner are polynomial-time algorithms and describe teacher/learner pairs for the classes of 1-decision lists and Horn sentences.

Connected through hyperedge HE-1 (induced by source paper "Learning Conjunctions of Horn Clauses"):
  with members:
    - "On Learning Visual Concepts and DNF Formulae"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Read-Once Formulas with Queries"
    - "Learning Boolean Read-Once Formulas over Generalized Bases"
    - "Asking Questions to Minimize Errors"
    - "On Learning Read-k-Satisfy-j DNF"

Connected through hyperedge HE-2 (induced by source paper "On the Sample Complexity of Weakly Learning"):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"

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

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