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: "Effects of Different Types of New Attribute on Constructive Induction"
  Abstract: This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, Mof-N, and Xof-N representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With different option settings, it can construct four different types of new attribute, but all other factors are fixed. The study reveals that conjunctive and disjunctive representations have very similar performance in terms of prediction accuracy and theory complexity on a variety of concepts. Moreover, the study demonstrates that the stronger representation power of Mof-N than conjunction and disjunction and the stronger representation power of Xof-N than these three types of new attribute can be reflected in the performance of decision tree learning.

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"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"

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"
    - "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"

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