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: "Multivariate Decision Trees"
  Abstract: COINS Technical Report 92-82 December 1992 Abstract Multivariate decision trees overcome a representational limitation of univariate decision trees: univariate decision trees are restricted to splits of the instance space that are orthogonal to the feature's axis. This paper discusses the following issues for constructing multivariate decision trees: representing a multivariate test, including symbolic and numeric features, learning the coefficients of a multivariate test, selecting the features to include in a test, and pruning of multivariate decision trees. We present some new and review some well-known methods for forming multivariate decision trees. The methods are compared across a variety of learning tasks to assess each method's ability to find concise, accurate decision trees. The results demonstrate that some multivariate methods are more effective than others. In addition, the experiments confirm that allowing multivariate tests improves the accuracy of the resulting decision tree over univariate trees.

Connected through hyperedge HE-1 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Dynamic Automatic Model Selection"
    - "Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings"
    - "Induction of Oblique Decision Trees"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Learning Decision Trees from Decision Rules:"
    - "The Estimation of Probabilities in Attribute Selection Measures for Decision Tree Induction"
    - "Learning Classification Trees"

Connected through hyperedge HE-2 (induced by source paper "Learning Polynomial Functions by Feature Construction"):
  with members:
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Dynamic Automatic Model Selection"
    - "Gain Adaptation Beats Least Squares?"
    - "Classification of EEG Signals Using a Sparse Polynomial Builder"

Connected through hyperedge HE-3 (induced by source paper "Multivariate versus Univariate Decision Trees"):
  with members:
    - "EE380L:Neural Networks for Pattern Recognition  POp Trees  under the guidance of"
    - "Constructing Conjunctions using Systematic Search on Decision Trees"
    - "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"
    - "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"

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