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: "Learning with Rare Cases and Small Disjuncts"
  Abstract: Systems that learn from examples often create a disjunctive concept definition. Small disjuncts are those disjuncts which cover only a few training examples. The problem with small disjuncts is that they are more error prone than large disjuncts. This paper investigates the reasons why small disjuncts are more error prone than large disjuncts. It shows that when there are rare cases within a domain, then factors such as attribute noise, missing attributes, class noise and training set size can result in small disjuncts being more error prone than large disjuncts and in rare cases being more error prone than common cases. This paper also assesses the impact that these error prone small disjuncts and rare cases have on inductive learning (i.e., on error rate). One key conclusion is that when low levels of attribute noise are applied only to the training set (the ability to learn the correct concept is being evaluated), rare cases within a domain are primarily responsible for making learning difficult.

Connected through hyperedge HE-1 (induced by source paper "The Problem with Noise and Small Disjuncts"):
  with members:
    - "Concept Learning and the Problem of Small"
    - "Chunking in soar: The anatomy of a general learn ing mechanism. Machine Learning, 1(1). Learning"

Connected through hyperedge HE-2 (induced by source paper "Chunking in soar: The anatomy of a general learn ing mechanism. Machine Learning, 1(1). Learning"):
  with members:
    - "A systematic description of greedy optimisation algorithms for cost sensitive generalisation"
    - "Learning Approximate Control Rules Of High Utility"
    - "The Problem with Noise and Small Disjuncts"

Connected through hyperedge HE-3 (induced by source paper "Concept Learning and the Problem of Small"):
  with members:
    - "Unifying Empirical and Explanation-Based Learning by Modeling the Utility of Learned Knowledge"
    - "Fossil: A Robust Relational Learner"
    - "The RISE System: Conquering Without Separating"
    - "The Problem with Noise and Small Disjuncts"
    - "Pessimistic and Optimistic Induction"
    - "On Learning Multiple Descriptions of a Concept"
    - "Using Partitioning to Speed Up Specific-to-General Rule Induction"

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