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 Boosting Ability of Top-Down Decision Tree Learning Algorithms provably optimal for decision tree"
  Abstract: We analyze the performance of top-down algorithms for decision tree learning, such as those employed by the widely used C4.5 and CART software packages. Our main result is a proof that such algorithms are boosting algorithms. By this we mean that if the functions that label the internal nodes of the decision tree can weakly approximate the unknown target function, then the top-down algorithms we study will amplify this weak advantage to build a tree achieving any desired level of accuracy. The bounds we obtain for this amplification show an interesting dependence on the splitting criterion used by the top-down algorithm. More precisely, if the functions used to label the internal nodes have error 1=2 fl as approximations to the target function, then for the splitting criteria used by CART and C4.5, trees of size (1=*) O(1=fl 2 * 2 ) and (1=*) O(log(1=*)=fl 2 ) (respectively) suffice to drive the error below *. Thus (for example), a small constant advantage over random guessing is amplified to any larger constant advantage with trees of constant size. For a new splitting criterion suggested by our analysis, the much stronger fl A preliminary version of this paper appears in Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, pages 459-468, ACM Press, 1996. Authors' addresses: M. Kearns, AT&T Research, 600 Mountain Avenue, Room 2A-423, Murray Hill, New Jersey 07974; electronic mail mkearns@research.att.com. Y. Mansour, Department of Computer Science, Tel Aviv University, Tel Aviv, Israel; electronic mail mansour@math.tau.ac.il. Y. Mansour was supported in part by the Israel Science Foundation, administered by the Israel Academy of Science and Humanities, and by a grant of the Israeli Ministry of Science and Technology.

Connected through hyperedge HE-1 (induced by source paper "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"
    - "An Experimental and Theoretical Comparison of Model Selection Methods on simple model selection problems, the"

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