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: "The Problem with Noise and Small Disjuncts"
  Abstract: Systems that learn from examples often create a disjunctive concept definition. The disjuncts in the concept definition which cover only a few training examples are referred to as small disjuncts. The problem with small disjuncts is that they are more error prone than large disjuncts, but may be necessary to achieve a high level of predictive accuracy [Holte, Acker, and Porter, 1989]. This paper extends previous work done on the problem of small disjuncts by taking noise into account. It investigates the assertion that it is hard to learn from noisy data because it is difficult to distinguish between noise and true exceptions. In the process of evaluating this assertion, insights are gained into the mechanisms by which noise affects learning. Two domains are investigated. The experimental results in this paper suggest that for both Shapiro's chess endgame domain [Shapiro, 1987] and for the Wisconsin breast cancer domain [Wolberg, 1990], the assertion is true, at least for low levels (5-10%) of class noise.

Connected through hyperedge HE-1 (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"
    - "Learning with Rare Cases and Small Disjuncts"

Connected through hyperedge HE-2 (induced by source paper "Learning with Rare Cases 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-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"
    - "Learning with Rare Cases 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