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 Read-Once Formulas with Queries"
  Abstract: A read-once formula is a boolean formula in which each variable occurs at most once. Such formulas are also called -formulas or boolean trees. This paper treats the problem of exactly identifying an unknown read-once formula using specific kinds of queries. The main results are a polynomial time algorithm for exact identification of monotone read-once formulas using only membership queries, and a polynomial time algorithm for exact identification of general read-once formulas using equivalence and membership queries (a protocol based on the notion of a minimally adequate teacher [1]). Our results improve on Valiant's previous results for read-once formulas [26]. We also show that no polynomial time algorithm using only membership queries or only equivalence queries can exactly identify all read-once formulas.

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 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 "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "An Interactive Model of Teaching"
    - "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning k-term DNF Formulas with an Incomplete Membership Oracle"

Connected through hyperedge HE-3 (induced by source paper "Learning k-term DNF Formulas with an Incomplete Membership Oracle"):
  with members:
    - "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"
    - "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"
    - "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle"
    - "Learning Conjunctions of Horn Clauses"
    - "Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)"

Connected through hyperedge HE-4 (induced by source paper "How Many Queries are Needed to Learn?"):
  with members:
    - "Learning Conjunctions of Horn Clauses"
    - "On the Power of Equivalence Queries"

Connected through hyperedge HE-5 (induced by source paper "Asking Questions to Minimize Errors"):
  with members:
    - "Simulating Access to Hidden Information while Learning"
    - "Learning Conjunctions of Horn Clauses"
    - "Learning Boolean Read-Once Formulas over Generalized Bases"

Connected through hyperedge HE-6 (induced by source paper "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "Learning DFA from Simple Examples"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Sequential PAC Learning"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "On the Sample Complexity of Weakly Learning"

Connected through hyperedge HE-7 (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"
    - "Asking Questions to Minimize Errors"

Connected through hyperedge HE-8 (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"
    - "An Interactive Model of Teaching"
    - "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"
    - "Learning Conjunctions of Horn Clauses"
    - "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