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: "Utilizing Prior Concepts for Learning"
  Abstract: The inductive learning problem consists of learning a concept given examples and non-examples of the concept. To perform this learning task, inductive learning algorithms bias their learning method. Here we discuss biasing the learning method to use previously learned concepts from the same domain. These learned concepts highlight useful information for other concepts in the domain. We describe a transference bias and present M-FOCL, a Horn clause relational learning algorithm, that utilizes this bias to learn multiple concepts. We provide preliminary empirical evaluation to show the effects of biasing previous information on noise-free and noisy data.

Connected through hyperedge HE-1 (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"
    - "Model selection using measure functions"
    - "Dlab: A Declarative Language Bias Formalism"

Connected through hyperedge HE-2 (induced by source paper "An investigation of noise-tolerant relational concept learning algorithms"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Incremental Reduced Error Pruning"
    - "Fossil: A Robust Relational Learner"
    - "A Comparison of Pruning Methods for Relational Concept Learning"
    - "Top-Down Pruning in Relational Learning"
    - "Stochastic search in inductive concept learning"

Connected through hyperedge HE-3 (induced by source paper "The Structure-Mapping Engine: Algorithm and Examples"):
  with members:
    - "The Case for Graph-Structured Representations"
    - "MAC/FAC: A Model of Similarity-based Retrieval"
    - "The Role of Generic Models in Conceptual Change"
    - "Adapting Abstract Knowledge"
    - "Role of Stories 1"
    - "Is analogical problem solving always analogical? The case for imitation. Second draft Is analogical problem"
    - "Functional Programming by Analogy"
    - "Modeling Analogical Problem Solving in a Production System Architecture"

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