human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"):
  Abstract: An Xof-N is a set containing one or more attribute-value pairs. For a given instance, its value corresponds to the number of its attribute-value pairs that are true. In this paper, we explore the characteristics and performance of continuous-valued Xof-N attributes versus nominal Xof-N attributes for constructive induction. Nominal Xof-Ns are more representationally powerful than continuous-valued Xof-Ns, but the former suffer the "fragmentation" problem, although some mechanisms such as subsetting can help to solve the problem. Two approaches to constructive induction using continuous-valued Xof-Ns are described. Continuous-valued Xof-Ns perform better than nominal ones on domains that need Xof-Ns with only one cut point. On domains that need Xof-N representations with more than one cut point, nominal Xof-Ns perform better than continuous-valued ones. Experimental results on a set of artificial and real-world domains support these statements.

Includes members:
  Member "Constructing Nominal Xof-N Attributes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction")
  Member "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction")
  Member "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction")
  Member "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees")
  Member "Effects of Different Types of New Attribute on Constructive Induction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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