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: "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.

Connected through hyperedge HE-1 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction"):
  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"

Connected through hyperedge HE-2 (induced by source paper "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"):
  with members:
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Constructing Nominal Xof-N Attributes"
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Draft Symbolic Representation of Neural Networks"
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"

Connected through hyperedge HE-3 (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-4 (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"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"
    - "Effects of Different Types of New Attribute on Constructive Induction"

Connected through hyperedge HE-5 (induced by source paper "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"):
  with members:
    - "Constructing Conjunctions using Systematic Search on Decision Trees"
    - "Constructing Nominal Xof-N Attributes"
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "A BENCHMARK FOR CLASSIFIER LEARNING"

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