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: "On Learning from Noisy and Incomplete Examples"
  Abstract: We investigate learnability in the PAC model when the data used for learning, attributes and labels, is either corrupted or incomplete. In order to prove our main results, we define a new complexity measure on statistical query (SQ) learning algorithms. The view of an SQ algorithm is the maximum over all queries in the algorithm, of the number of input bits on which the query depends. We show that a restricted view SQ algorithm for a class is a general sufficient condition for learnability in both the models of attribute noise and covered (or missing) attributes. We further show that since the algorithms in question are statistical, they can also simultaneously tolerate classification noise. Classes for which these results hold, and can therefore be learned with simultaneous attribute noise and classification noise, include k-DNF, k-term-DNF by DNF representations, conjunctions with few relevant variables, and over the uniform distribution, decision lists. These noise models are the first PAC models in which all training data, attributes and labels, may be corrupted by a random process. Previous researchers had shown that the class of k-DNF is learnable with attribute noise if the attribute noise rate is known exactly. We show that all of our attribute noise learnabil-ity results, either with or without classification noise, also hold when the exact noise rate is not Appeared in Proceedings of the Eighth Annual ACM Conference on Computational Learning Theory. ACM Press, July 1995. known, provided that the learner instead has a polynomially good approximation of the noise rate. In addition, we show that the results also hold when there is not one single noise rate, but a distinct noise rate for each attribute. Our results for learning with random covering do not require the learner to be told even an approximation of the covering rate and in addition hold in the setting with distinct covering rates for each attribute. Finally, we give lower bounds on the number of examples required for learning in the presence of attribute noise or covering.

Connected through hyperedge HE-1 (induced by source paper "Improved Noise-Tolerant Learning and Generalized Statistical Queries"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"

Connected through hyperedge HE-2 (induced by source paper "Statistical Queries and Faulty PAC Oracles"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "Learning in the Presence of Malicious Errors"
    - "On Learning Visual Concepts and DNF Formulae"

Connected through hyperedge HE-3 (induced by source paper "Pac Learning, Noise, and Geometry"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "Learning from Incomplete Boundary Queries Using Split Graphs and Hypergraphs (Extended Abstract)"

Connected through hyperedge HE-4 (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"
    - "Improved Noise-Tolerant Learning and Generalized Statistical Queries"

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