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: "Recursive Automatic Algorithm Selection for Inductive Learning"
  Abstract: COINS Technical Report 94-61 August 1994

Connected through hyperedge HE-1 (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)"
    - "Multivariate versus Univariate Decision Trees"
    - "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-2 (induced by source paper "Learning Polynomial Functions by Feature Construction"):
  with members:
    - "Multivariate Decision Trees"
    - "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"
    - "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)"
    - "Constructing Nominal Xof-N Attributes"
    - "Dynamic Automatic Model Selection"

Connected through hyperedge HE-4 (induced by source paper "Generalizing from Case Studies: A Case Study"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Bias and the Probability of Generalization"
    - "Systematic Evaluation of Design Decisions in CBR Systems"
    - "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"
    - "Dynamical Selection of Learning Algorithms"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

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