human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "On Learning Visual Concepts and DNF Formulae":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning in the Presence of Malicious Errors")
  Member "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning Conjunctions of Horn Clauses")
  Member "Learning Conjunctions of Horn Clauses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle")
  Member "Learning Read-Once Formulas with Queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning Conjunctions of Horn Clauses")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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