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 versus Univariate Decision Trees"
  Abstract: COINS Technical Report 92-8 January 1992 Abstract In this paper we present a new multivariate decision tree algorithm LMDT, which combines linear machines with decision trees. LMDT constructs each test in a decision tree by training a linear machine and then eliminating irrelevant and noisy variables in a controlled manner. To examine LMDT's ability to find good generalizations we present results for a variety of domains. We compare LMDT empirically to a univariate decision tree algorithm and observe that when multivariate tests are the appropriate bias for a given data set, LMDT finds small accurate trees.

Connected through hyperedge HE-1 (induced by source paper "Multivariate Decision Trees"):
  with members:
    - "Learning Polynomial Functions by Feature Construction"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"

Connected through hyperedge HE-2 (induced by source paper "Recursive Automatic Algorithm Selection for Inductive Learning"):
  with members:
    - "Dynamic Automatic Model Selection"
    - "Learning Polynomial Functions by Feature Construction"
    - "Generalizing from Case Studies: A Case Study"

Connected through hyperedge HE-3 (induced by source paper "EE380L:Neural Networks for Pattern Recognition  POp Trees  under the guidance of"):
  with members:
    (none)

Connected through hyperedge HE-4 (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)"
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Learning Polynomial Functions by Feature Construction"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "Constructing Nominal Xof-N Attributes"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"
    - "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"
    - "Effects of Different Types of New Attribute on Constructive Induction"

Connected through hyperedge HE-6 (induced by source paper "Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract)"):
  with members:
    - "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"

Connected through hyperedge HE-7 (induced by source paper "Learning NonLinearly Separable Boolean Functions With Linear Threshold Unit Trees and Madaline-Style Networks"):
  with members:
    - "Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract)"
    - "Induction of Selective Bayesian Classifiers"

Connected through hyperedge HE-8 (induced by source paper "Constructing Conjunctions using Systematic Search on Decision Trees"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR 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