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: "Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract)"
  Abstract: We investigate the generation of neural networks through the induction of binary trees of threshold logic units (TLUs). Initially, we describe the framework for our tree construction algorithm and how such trees can be transformed into an isomorphic neural network topology. Several methods for learning the linear discriminant functions at each node of the tree structure are examined and shown to produce accuracy results that are comparable to classical information theoretic methods for constructing decision trees (which use single feature tests at each node). Our TLU trees, however, are smaller and thus easier to understand. Moreover, we show that it is possible to simultaneously learn both the topology and weight settings of a neural network simply using the training data set that we are given.

Connected through hyperedge HE-1 (induced by source paper "Multivariate versus Univariate Decision Trees"):
  with members:
    - "EE380L:Neural Networks for Pattern Recognition  POp Trees  under the guidance of"
    - "Multivariate Decision Trees"
    - "Constructing Conjunctions using Systematic Search on Decision Trees"
    - "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Constructing Nominal Xof-N Attributes"
    - "Dynamic Automatic Model Selection"

Connected through hyperedge HE-2 (induced by source paper "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"):
  with members:
    - "Multivariate versus Univariate Decision Trees"
    - "Induction of Selective Bayesian Classifiers"

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