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: "MLC A Machine Learning Library in C"
  Abstract: We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC ++ is an attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC ++ aims to solve, the design of MLC ++ , and the current functionality.

Connected through hyperedge HE-1 (induced by source paper "Applying a Machine Learning Workbench: Experience with Agricultural Databases"):
  with members:
    - "EMERALD: An Integrated System of Machine Learning and Discovery Programs to Support AI Education and"

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"
    - "Model selection using measure functions"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "Selection of Relevant Features and Examples in Machine Learning"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-4 (induced by source paper "Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting"):
  with members:
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-5 (induced by source paper "Error-Based and Entropy-Based Discretization of Continuous Features"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "Supervised and Unsupervised Discretization of Continuous Features"
    - "Targeting Business Users with Decision Table Classifiers"

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