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: "On Learning Read-k-Satisfy-j DNF"
  Abstract: We study the learnability of Read-k-Satisfy-j (RkSj) DNF formulas. These are boolean formulas in disjunctive normal form (DNF), in which the maximum number of occurrences of a variable is bounded by k, and the number of terms satisfied by any assignment is at most j. After motivating the investigation of this class of DNF formulas, we present an algorithm that with high probability finds a DNF formula that is logically equivalent to any unknown RkSj DNF formula to be learned. The algorithm uses the well-studied protocol of equivalence and membership queries, and runs in polynomial time for k j = O( log n log log n ), where n is the number of input variables.

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"
    - "Asking Questions to Minimize Errors"

Connected through hyperedge HE-2 (induced by source paper "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"):
  with members:
    - "On Learning Visual Concepts and DNF Formulae"
    - "Toward Efficient Agnostic Learning"
    - "A Quantum Computational Learning Algorithm"
    - "Learning Conjunctions of Horn Clauses"
    - "Implementation Issues in the Fourier Transform Algorithm"
    - "An O(n log log n Learning Algorithm for DNF under the Uniform Distribution"
    - "Learning Using Group Representations (Extended Abstract)"

Connected through hyperedge HE-3 (induced by source paper "On Learning Visual Concepts and DNF Formulae"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Statistical Queries and Faulty PAC Oracles"
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"
    - "Learning Conjunctions of Horn Clauses"

Connected through hyperedge HE-4 (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"
    - "Asking Questions to Minimize Errors"
    - "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)"

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