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: "Dynamic Automatic Model Selection"
  Abstract: COINS Technical Report 92-30 February 1992 Abstract The problem of how to learn from examples has been studied throughout the history of machine learning, and many successful learning algorithms have been developed. A problem that has received less attention is how to select which algorithm to use for a given learning task. The ability of a chosen algorithm to induce a good generalization depends on how appropriate the model class underlying the algorithm is for the given task. We define an algorithm's model class to be the representation language it uses to express a generalization of the examples. Supervised learning algorithms differ in their underlying model class and in how they search for a good generalization. Given this characterization, it is not surprising that some algorithms find better generalizations for some, but not all tasks. Therefore, in order to find the best generalization for each task, an automated learning system must search for the appropriate model class in addition to searching for the best generalization within the chosen class. This thesis proposal investigates the issues involved in automating the selection of the appropriate model class. The presented approach has two facets. Firstly, the approach combines different model classes in the form of a model combination decision tree, which allows the best representation to be found for each subconcept of the learning task. Secondly, which model class is the most appropriate is determined dynamically using a set of heuristic rules. Explicit in each rule are the conditions in which a particular model class is appropriate and if it is not, what should be done next. In addition to describing the approach, this proposal describes how the approach will be evaluated in order to demonstrate that it is both an efficient and effective method for automatic model selection.

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"
    - "Multivariate Decision Trees"
    - "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 "Dynamical Selection of Learning Algorithms"):
  with members:
    - "Generalizing from Case Studies: A Case Study"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"

Connected through hyperedge HE-3 (induced by source paper "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Generalizing from Case Studies: A Case Study"
    - "Concept Learning and Flexible Weighting"

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

Connected through hyperedge HE-5 (induced by source paper "Comparison of Regression Methods, Symbolic Induction Methods and Neural Networks in Morbidity Diagnosis and Mortality"):
  with members:
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"

Connected through hyperedge HE-6 (induced by source paper "Learning Polynomial Functions by Feature Construction"):
  with members:
    - "Multivariate Decision Trees"
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Gain Adaptation Beats Least Squares?"
    - "Classification of EEG Signals Using a Sparse Polynomial Builder"

Connected through hyperedge HE-7 (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"
    - "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"

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