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 "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"):
  Abstract: We derive general bounds on the complexity of learning in the Statistical Query model and in the PAC model with classification noise. We do so by considering the problem of boosting the accuracy of weak learning algorithms which fall within the Statistical Query model. This new model was introduced by Kearns [12] to provide a general framework for efficient PAC learning in the presence of classification noise. We first show a general scheme for boosting the accuracy of weak SQ learning algorithms, proving that weak SQ learning is equivalent to strong SQ learning. The boosting is efficient and is used to show our main result of the first general upper bounds on the complexity of strong SQ learning. Specifically, we derive simultaneous upper bounds with respect to * on the number of queries, O(log 2 1 * ), the Vapnik-Chervonenkis dimension of the query space, O(log 1 * ), and the inverse of the minimum tolerance, O( 1 * log 1 * ). In addition, we show that these general upper bounds are nearly optimal by describing a class of learning problems for which we simultaneously lower bound the number of queries by (log 1 * ) We further apply our boosting results in the SQ model to learning in the PAC model with classification noise. Since nearly all PAC learning algorithms can be cast in the SQ model, we can apply our boosting techniques to convert these PAC algorithms into highly efficient SQ algorithms. By simulating these efficient SQ algorithms in the PAC model with classification noise, we show that nearly all PAC algorithms can be converted into highly efficient PAC algorithms which tolerate classification noise. We give an upper bound on the sample complexity of these noise-tolerant PAC algorithms which is nearly optimal with respect to the noise rate. We also give upper bounds on space complexity and hypothesis size and show that these two measures are in fact independent of the noise rate. We note that the running times of these noise-tolerant PAC algorithms are efficient. This sequence of simulations also demonstrates that it is possible to boost the accuracy of nearly all PAC algorithms even in the presence of noise. This provides a partial answer to an open problem of Schapire [15] and the first theoretical evidence for an empirical result of Drucker, Schapire and Simard [4].

Includes members:
  Member "25 Learning in Hybrid Noise Environments Using Statistical Queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Improved Noise-Tolerant Learning and Generalized Statistical Queries")
  Member "Learning Sparse Perceptrons":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning to Represent Codons: A Challenge Problem for Constructive Induction")
  Member "On Learning Visual Concepts and DNF Formulae":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning in the Presence of Malicious Errors")
  Member "On the Sample Complexity of Noise-Tolerant Learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "Boosting a weak learning algorithm by majority To be published in Information and Computation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Efficient Distribution-free Learning of Probabilistic Concepts")

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