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 the Sample Complexity of Noise-Tolerant Learning"
  Abstract: In this paper, we further characterize the complexity of noise-tolerant learning in the PAC model. Specifically, we show a general lower bound of log(1=ffi) on the number of examples required for PAC learning in the presence of classification noise. Combined with a result of Simon, we effectively show that the sample complexity of PAC learning in the presence of classification noise is VC(F) "(12) 2 : Furthermore, we demonstrate the optimality of the general lower bound by providing a noise-tolerant learning algorithm for the class of symmetric Boolean functions which uses a sample size within a constant factor of this bound. Finally, we note that our general lower bound compares favorably with various general upper bounds for PAC learning in the presence of classification noise.

Connected through hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Sequential PAC Learning"
    - "Sifting informative examples from a random source."
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "Inductive Logic Programming"
    - "Pac Learning, Noise, and Geometry"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-2 (induced by source paper "Self bounding learning algorithms"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"

Connected through hyperedge HE-3 (induced by source paper "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "Learning Sparse Perceptrons"
    - "On Learning Visual Concepts and DNF Formulae"
    - "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