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: "Improved Noise-Tolerant Learning and Generalized Statistical Queries"
  Abstract: The statistical query learning model can be viewed as a tool for creating (or demonstrating the existence of) noise-tolerant learning algorithms in the PAC model. The complexity of a statistical query algorithm, in conjunction with the complexity of simulating SQ algorithms in the PAC model with noise, determine the complexity of the noise-tolerant PAC algorithms produced. Although roughly optimal upper bounds have been shown for the complexity of statistical query learning, the corresponding noise-tolerant PAC algorithms are not optimal due to inefficient simulations. In this paper we provide both improved simulations and a new variant of the statistical query model in order to overcome these inefficiencies. We improve the time complexity of the classification noise simulation of statistical query algorithms. Our new simulation has a roughly optimal dependence on the noise rate. We also derive a simpler proof that statistical queries can be simulated in the presence of classification noise. This proof makes fewer assumptions on the queries themselves and therefore allows one to simulate more general types of queries. We also define a new variant of the statistical query model based on relative error, and we show that this variant is more natural and strictly more powerful than the standard additive error model. We demonstrate efficient PAC simulations for algorithms in this new model and give general upper bounds on both learning with relative error statistical queries and PAC simulation. We show that any statistical query algorithm can be simulated in the PAC model with malicious errors in such a way that the resultant PAC algorithm has a roughly optimal tolerable malicious error rate and sample complexity. Finally, we generalize the types of queries allowed in the statistical query model. We discuss the advantages of allowing these generalized queries and show that our results on improved simulations also hold for these queries. This paper is available from the Center for Research in Computing Technology, Division of Applied Sciences, Harvard University as technical report TR-17-94.

Connected through hyperedge HE-1 (induced by source paper "On Learning from Noisy and Incomplete Examples"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "Statistical Queries and Faulty PAC Oracles"
    - "Pac Learning, Noise, and Geometry"

Connected through hyperedge HE-2 (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"
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "On Learning from Noisy and Incomplete Examples"

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