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 the Sample Complexity of Noise-Tolerant Learning"):
  Abstract: In this paper, we further characterize the complexity of noise-tolerant learning in the PAC model. Specifically, we show a general lower bound of log(1=ffi) on the number of examples required for PAC learning in the presence of classification noise. Combined with a result of Simon, we effectively show that the sample complexity of PAC learning in the presence of classification noise is VC(F) "(12) 2 : Furthermore, we demonstrate the optimality of the general lower bound by providing a noise-tolerant learning algorithm for the class of symmetric Boolean functions which uses a sample size within a constant factor of this bound. Finally, we note that our general lower bound compares favorably with various general upper bounds for PAC learning in the presence of classification noise.

Includes members:
  Member "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "25 Learning in Hybrid Noise Environments Using Statistical Queries")
  Member "Self bounding learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Rigorous Learning Curve Bounds from Statistical Mechanics")
  Member "A General Lower Bound on the Number of Examples Needed for Learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Inductive Logic Programming")

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