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: "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"
  Abstract: This paper discusses an approach of constructing new attributes based on decision trees and production rules. It can improve the concepts learned in the form of decision trees by simplifying them and improving their predictive accuracy. In addition, this approach can distinguish relevant primitive attributes from irrelevant primitive attributes.

Connected through hyperedge HE-1 (induced by source paper "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"):
  with members:
    - "Constructing Nominal Xof-N Attributes"
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Effects of Different Types of New Attribute on Constructive Induction"

Connected through hyperedge HE-2 (induced by source paper "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"):
  with members:
    - "Constructing Conjunctions using Systematic Search on Decision Trees"
    - "Constructing Nominal Xof-N Attributes"
    - "Extending Theory Refinement to M-of-N Rules"
    - "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach"
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"
    - "Parity: The Problem that Won't Go Away"
    - "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"

Connected through hyperedge HE-3 (induced by source paper "Constructing Nominal Xof-N Attributes"):
  with members:
    - "Multivariate versus Univariate Decision Trees"
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"
    - "Effects of Different Types of New Attribute on Constructive Induction"

Connected through hyperedge HE-4 (induced by source paper "A BENCHMARK FOR CLASSIFIER LEARNING"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

Connected through hyperedge HE-5 (induced by source paper "Constructing Conjunctions using Systematic Search on Decision Trees"):
  with members:
    - "Multivariate versus Univariate Decision Trees"
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"

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