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: "Decision Trees: Equivalence and Propositional Operations"
  Abstract: For the well-known concept of decision trees as it is used for inductive inference we study the natural concept of equivalence: two decision trees are equivalent if and only if they represent the same hypothesis. We present a simple efficient algorithm to establish whether two decision trees are equivalent or not. The complexity of this algorithm is bounded by the product of the sizes of both decision trees. The hypothesis represented by a decision tree is essentially a boolean function, just like a proposition. Although every boolean function can be represented in this way, we show that disjunctions and conjunctions of decision trees can not efficiently be represented as decision trees, and simply shaped propositions may require exponential size for representation as de cision trees.

Connected through hyperedge HE-1 (induced by source paper "Machine Learning Research: Four Current Directions"):
  with members:
    - "A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment"

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