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 "Warning: missing six few referencesfixed in proceedings. Learning with Queries but Incomplete Information (Extended Abstract)"):
  Abstract: We investigate learning with membership and equivalence queries assuming that the information provided to the learner is incomplete. By incomplete we mean that some of the membership queries may be answered by I don't know. This model is a worst-case version of the incomplete membership query model of Angluin and Slonim. It attempts to model practical learning situations, including an experiment of Lang and Baum that we describe, where the teacher may be unable to answer reliably some queries that are critical for the learning algorithm. We present algorithms to learn monotone k-term DNF with membership queries only, and to learn monotone DNF with membership and equivalence queries. Compared to the complete information case, the query complexity increases by an additive term linear in the number of I don't know answers received. We also observe that the blowup in the number of queries can in general be exponential for both our new model and the incomplete membership model.

Includes members:
  Member "Simulating Access to Hidden Information while Learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "An Interactive Model of Teaching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle")
  Member "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Pac Learning, Noise, and Geometry")
  Member "Learning Conjunctions of Horn Clauses":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "Learning Conjunctions of Horn Clauses")
  Member "Learning k-term DNF Formulas with an Incomplete Membership Oracle":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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