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: "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].

Connected through hyperedge HE-1 (induced by source paper "25 Learning in Hybrid Noise Environments Using Statistical Queries"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Statistical Queries and Faulty PAC Oracles"
    - "Improved Noise-Tolerant Learning and Generalized Statistical Queries"
    - "On Learning from Noisy and Incomplete Examples"

Connected through hyperedge HE-2 (induced by source paper "Learning Sparse Perceptrons"):
  with members:
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Learning to Represent Codons: A Challenge Problem for Constructive Induction"

Connected through hyperedge HE-3 (induced by source paper "Boosting a weak learning algorithm by majority To be published in Information and Computation"):
  with members:
    - "Sifting informative examples from a random source."
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Learning From a Population of Hypotheses"
    - "Game Theory, On-line Prediction and Boosting"
    - "On the Sample Complexity of Weakly Learning"
    - "A Quantum Computational Learning Algorithm"

Connected through hyperedge HE-4 (induced by source paper "On the Sample Complexity of Noise-Tolerant Learning"):
  with members:
    - "Self bounding learning algorithms"
    - "A General Lower Bound on the Number of Examples Needed for Learning"

Connected through hyperedge HE-5 (induced by source paper "On Learning Visual Concepts and DNF Formulae"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Statistical Queries and Faulty PAC Oracles"
    - "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"
    - "Learning Conjunctions of Horn Clauses"
    - "On Learning Read-k-Satisfy-j DNF"

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