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: "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"
  Abstract: This paper investigates an algorithm for the construction of decisions trees comprised of linear threshold units and also presents a novel algorithm for the learning of non-linearly separable boolean functions using Madaline-style networks which are isomorphic to decision trees. The construction of such networks is discussed, and their performance in learning is compared with standard BackPropagation on a sample problem in which many irrelevant attributes are introduced. Littlestone's Winnow algorithm is also explored within this architecture as a means of learning in the presence of many irrelevant attributes. The learning ability of this Madaline-style architecture on nonoptimal (larger than necessary) networks is also explored.

Connected through hyperedge HE-1 (induced by source paper "Induction of Selective Bayesian Classifiers"):
  with members:
    - "Searching for dependencies in Bayesian classifiers j A n V n j If the attributes"
    - "Learning Bayesian Prototype Trees by Simulated Annealing"
    - "Learning in neural networks with Bayesian prototypes"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "Efficient Learning of Selective Bayesian Network Classifiers"
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"

Connected through hyperedge HE-2 (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"
    - "Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract)"
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Constructing Nominal Xof-N Attributes"
    - "Dynamic Automatic Model Selection"

Connected through hyperedge HE-3 (induced by source paper "Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract)"):
  with members:
    - "Multivariate versus Univariate 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: Neural_Networks