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: "Linear Machine Decision Trees"
  Abstract: COINS Technical Report 91-10 January 1991 Abstract This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks.

Connected through hyperedge HE-1 (induced by source paper "An Improved Algorithm for Incremental Induction of Decision Trees"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Theory Revision in Fault Hierarchies"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Learning Classification Trees"
    - "Relating Relational Learning Algorithms"
    - "Geometric Comparison of Classifications and Rule Sets*"

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: Neural_Networks