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: "Model selection using measure functions"
  Abstract: The concept of measure functions for generalization performance is suggested. This concept provides an alternative way of selecting and evaluating learned models (classifiers). In addition, it makes it possible to state a learning problem as a computational problem. The the known prior (meta-)knowledge about the problem domain is captured in a measure function that, to each possible combination of a training set and a classifier, assigns a value describing how good the classifier is. The computational problem is then to find a classifier maximizing the measure function. We argue that measure functions are of great value for practical applications. Besides of being a tool for model selection, they: (i) force us to make explicit the relevant prior knowledge about the learning problem at hand, (ii) provide a deeper understanding of existing algorithms, and (iii) help us in the construction of problem-specific algorithms. We illustrate the last point by suggesting a novel algorithm based on incremental search for a classifier that optimizes a given measure function.

Connected through hyperedge HE-1 (induced by source paper "Beyond predictive accuracy: what?"):
  with members:
    - "Using Precepts to Augment Training Set Learning an input whose value is don't-care in some"

Connected through hyperedge HE-2 (induced by source paper "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"):
  with members:
    - "On learning hierarchical classifications"
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation"
    - "Characterizing the generalization performance of model selection strategies"
    - "A New Metric-Based Approach to Model Selection"
    - "Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting"
    - "MLC A Machine Learning Library in C"

Connected through hyperedge HE-3 (induced by source paper "Evaluation and Selection of Biases in Machine Learning"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Evolution-based Discovery of Hierarchical Behaviors"
    - "#1 Robust Feature Selection Algorithms"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Dlab: A Declarative Language Bias Formalism"
    - "Utilizing Prior Concepts for Learning"

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