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: "Self bounding learning algorithms"
  Abstract: Most of the work which attempts to give bounds on the generalization error of the hypothesis generated by a learning algorithm is based on methods from the theory of uniform convergence. These bounds are a-priori bounds that hold for any distribution of examples and are calculated before any data is observed. In this paper we propose a different approach for bounding the generalization error after the data has been observed. A self-bounding learning algorithm is an algorithm which, in addition to the hypothesis that it outputs, outputs a reliable upper bound on the generalization error of this hypothesis. We first explore the idea in the statistical query learning framework of Kearns [10]. After that we give an explicit self bounding algorithm for learning algorithms that are based on local search.

Connected through hyperedge HE-1 (induced by source paper "Rigorous Learning Curve Bounds from Statistical Mechanics"):
  with members:
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Markov Decision Processes in Large State Spaces"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Where Genetic Algorithms Excel"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with"

Connected through hyperedge HE-2 (induced by source paper "Pessimistic decision tree pruning based on tree size"):
  with members:
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"
    - "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid"

Connected through hyperedge HE-3 (induced by source paper "On the Sample Complexity of Noise-Tolerant Learning"):
  with members:
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "A General Lower Bound on the Number of Examples Needed for Learning"

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