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: "Asking Questions to Minimize Errors"
  Abstract: A number of efficient learning algorithms achieve exact identification of an unknown function from some class using membership and equivalence queries. Using a standard transformation such algorithms can easily be converted to on-line learning algorithms that use membership queries. Under such a transformation the number of equivalence queries made by the query algorithm directly corresponds to the number of mistakes made by the on-line algorithm. In this paper we consider several of the natural classes known to be learnable in this setting, and investigate the minimum number of equivalence queries with accompanying counterexamples (or equivalently the minimum number of mistakes in the on-line model) that can be made by a learning algorithm that makes a polynomial number of membership queries and uses polynomial computation time. We are able both to reduce the number of equivalence queries used by the previous algorithms and often to prove matching lower bounds. As an example, consider the class of DNF formulas over n variables with at most k = O(log n) terms. Previously, the algorithm of Blum and Rudich [BR92] provided the best known upper bound of 2 O(k) log n for the minimum number of equivalence queries needed for exact identification. We greatly improve on this upper bound showing that exactly k counterexamples are needed if the learner knows k a priori and exactly k +1 counterexamples are needed if the learner does not know k a priori. This exactly matches known lower bounds [BC92]. For many of our results we obtain a complete characterization of the tradeoff between the number of membership and equivalence queries needed for exact identification. The classes we consider here are monotone DNF formulas, Horn sentences, O(log n)-term DNF formulas, read-k sat-j DNF formulas, read-once formulas over various bases, and deterministic finite automata.

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"
    - "Teaching a Smarter Learner"
    - "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"
    - "On Learning Read-k-Satisfy-j DNF"

Connected through hyperedge HE-2 (induced by source paper "Learning Read-Once Formulas with Queries"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Boolean Read-Once Formulas over Generalized Bases"
    - "On Learning Read-k-Satisfy-j DNF"
    - "How Many Queries are Needed to Learn?"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"
    - "Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)"

Connected through hyperedge HE-3 (induced by source paper "Learning Boolean Read-Once Formulas over Generalized Bases"):
  with members:
    - "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with Queries"

Connected through hyperedge HE-4 (induced by source paper "Simulating Access to Hidden Information while Learning"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "On the Complexity of Function Learning"
    - "Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)"

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