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 from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"
  Abstract: We consider learnability with membership queries in the presence of incomplete information. In the incomplete boundary query model introduced by Blum et al. [7], it is assumed that membership queries on instances near the boundary of the target concept may receive a "don't know" answer. We show that zero-one threshold functions are efficiently learnable in this model. The learning algorithm uses split graphs when the boundary region has radius 1, and their generalization to split hypergraphs (for which we give a split-finding algorithm) when the boundary region has constant radius greater than 1. We use a notion of indistinguishability of concepts that is appropriate for this model.

Connected through hyperedge HE-1 (induced by source paper "Pac Learning, Noise, and Geometry"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "On Learning from Noisy and Incomplete Examples"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "PAC-Learning PROLOG clauses with or without errors"

Connected through hyperedge HE-2 (induced by source paper "Learning k-term DNF Formulas with an Incomplete Membership Oracle"):
  with members:
    - "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"
    - "Learning Read-Once Formulas with Queries"
    - "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 With Unreliable Boundary Queries"):
  with members:
    - "PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract)"
    - "Learning to model sequences generated by switching distributions"

Connected through hyperedge HE-4 (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 Conjunctions of Horn Clauses"
    - "Learning Read-Once Formulas with Queries"
    - "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