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: "25 Learning in Hybrid Noise Environments Using Statistical Queries"
  Abstract: We consider formal models of learning from noisy data. Specifically, we focus on learning in the probability approximately correct model as defined by Valiant. Two of the most widely studied models of noise in this setting have been classification noise and malicious errors. However, a more realistic model combining the two types of noise has not been formalized. We define a learning environment based on a natural combination of these two noise models. We first show that hypothesis testing is possible in this model. We next describe a simple technique for learning in this model, and then describe a more powerful technique based on statistical query learning. We show that the noise tolerance of this improved technique is roughly optimal with respect to the desired learning accuracy and that it provides a smooth tradeoff between the tolerable amounts of the two types of noise. Finally, we show that statistical query simulation yields learning algorithms for other combinations of noise models, thus demonstrating that statistical query specification truly An important goal of research in machine learning is to determine which tasks can be automated, and for those which can, to determine their information and computation requirements. One way to answer these questions is through the development and investigation of formal models of machine learning which capture the task of learning under plausible assumptions. In this work, we consider the formal model of learning from examples called "probably approximately correct" (PAC) learning as defined by Valiant [Val84]. In this setting, a learner attempts to approximate an unknown target concept simply by viewing positive and negative examples of the concept. An adversary chooses, from some specified function class, a hidden f0; 1g-valued target function defined over some specified domain of examples and chooses a probability distribution over this domain. The goal of the learner is to output in both polynomial time and with high probability, an hypothesis which is "close" to the target function with respect to the distribution of examples. The learner gains information about the target function and distribution by interacting with an example oracle. At each request by the learner, this oracle draws an example randomly according to the hidden distribution, labels it according to the hidden target function, and returns the labelled example to the learner. A class of functions F is said to be PAC learnable if captures the generic fault tolerance of a learning algorithm.

Connected through hyperedge HE-1 (induced by source paper "Improved Noise-Tolerant Learning and Generalized Statistical Queries"):
  with members:
    - "On Learning from Noisy and Incomplete Examples"

Connected through hyperedge HE-2 (induced by source paper "On Learning from Noisy and Incomplete Examples"):
  with members:
    - "Statistical Queries and Faulty PAC Oracles"
    - "Improved Noise-Tolerant Learning and Generalized Statistical Queries"
    - "Pac Learning, Noise, and Geometry"

Connected through hyperedge HE-3 (induced by source paper "Learning in the Presence of Malicious Errors"):
  with members:
    - "Statistical Queries and Faulty PAC Oracles"
    - "On Learning Visual Concepts and DNF Formulae"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Pac Learning, Noise, and Geometry"
    - "Tracking Drifting Concepts By Minimizing Disagreements"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "On Learning Conjunctions with Malicious Noise"
    - "Toward Efficient Agnostic Learning"

Connected through hyperedge HE-4 (induced by source paper "Statistical Queries and Faulty PAC Oracles"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "On Learning from Noisy and Incomplete Examples"
    - "On Learning Visual Concepts and DNF Formulae"

Connected through hyperedge HE-5 (induced by source paper "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"):
  with members:
    - "Learning Sparse Perceptrons"
    - "On Learning Visual Concepts and DNF Formulae"
    - "On the Sample Complexity of Noise-Tolerant Learning"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"

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