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: "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
  Abstract: We exhibit a theoretically founded algorithm T2 for agnostic PAC-learning of decision trees of at most 2 levels, whose computation time is almost linear in the size of the training set. We evaluate the performance of this learning algorithm T2 on 15 common real-world datasets, and show that for most of these datasets T2 provides simple decision trees with little or no loss in predictive power (compared with C4.5). In fact, for datasets with continuous attributes its error rate tends to be lower than that of C4.5. To the best of our knowledge this is the first time that a PAC-learning algorithm is shown to be applicable to real-world classification problems. Since one can prove that T2 is an agnostic PAC-learning algorithm, T2 is guaranteed to produce close to optimal 2-level decision trees from sufficiently large training sets for any (!) distribution of data. In this regard T2 differs strongly from all other learning algorithms that are considered in applied machine learning, for which no guarantee can be given about their performance on new datasets. We also demonstrate that this algorithm T2 can be used as a diagnostic tool for the investigation of the expressive limits of 2-level decision trees. Finally, T2, in combination with new bounds on the VC-dimension of decision trees of bounded depth that we derive, provides us now for the first time with the tools necessary for comparing learning curves of decision trees for real-world datasets with the theoretical estimates of PAC learning theory.

Connected through hyperedge HE-1 (induced by source paper "Stochastic Propositionalization of Non-Determinate Background Knowledge"):
  with members:
    - "Learning Trees and Rules with Set-valued Features"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "Inverse entailment and Progol"

Connected through hyperedge HE-2 (induced by source paper "Learning Active Classifiers"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Exploiting the Omission of Irrelevant Data"
    - "Learning Default Concepts"
    - "Learning to Reason with a Restricted View"

Connected through hyperedge HE-3 (induced by source paper "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"):
  with members:
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"
    - "Inverse entailment and Progol"

Connected through hyperedge HE-4 (induced by source paper "Pessimistic decision tree pruning based on tree size"):
  with members:
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Self bounding learning algorithms"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "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-5 (induced by source paper "Error-Based and Entropy-Based Discretization of Continuous Features"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "MLC A Machine Learning Library in C"
    - "Supervised and Unsupervised Discretization of Continuous Features"
    - "Targeting Business Users with Decision Table Classifiers"

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