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 Polynomial Functions by Feature Construction"
  Abstract: We present a method for learning higher-order polynomial functions from examples using linear regression and feature construction. Regression is used on a set of training instances to produce a weight vector for a linear function over the feature set. If this hypothesis is imperfect, a new feature is constructed by forming the product of the two features that most effectively predict the squared error of the current hypothesis. The algorithm is then repeated. In an extension to this method, the specific pair of features to combine is selected by measuring their joint ability to predict the hypothesis' error.

Connected through hyperedge HE-1 (induced by source paper "Gain Adaptation Beats Least Squares?"):
  with members:
    - "From Isolation to Cooperation: An Alternative View of a System of Experts"
    - "Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta"
    - "Least-Squares Temporal Difference Learning"

Connected through hyperedge HE-2 (induced by source paper "Multivariate Decision Trees"):
  with members:
    - "Multivariate versus Univariate Decision Trees"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"

Connected through hyperedge HE-3 (induced by source paper "Classification of EEG Signals Using a Sparse Polynomial Builder"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Recursive Automatic Algorithm Selection for Inductive Learning"):
  with members:
    - "Multivariate versus Univariate Decision Trees"
    - "Dynamic Automatic Model Selection"
    - "Generalizing from Case Studies: A Case Study"

Connected through hyperedge HE-5 (induced by source paper "Dynamic Automatic Model Selection"):
  with members:
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "Multivariate versus Univariate Decision Trees"
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Dynamical Selection of Learning Algorithms"
    - "Comparison of Regression Methods, Symbolic Induction Methods and Neural Networks in Morbidity Diagnosis and Mortality"

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