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: "Error Reduction through Learning Multiple Descriptions"
  Abstract: Learning multiple descriptions for each class in the data has been shown to reduce generalization error but the amount of error reduction varies greatly from domain to domain. This paper presents a novel empirical analysis that helps to understand this variation. Our hypothesis is that the amount of error reduction is linked to the "degree to which the descriptions for a class make errors in a correlated manner." We present a precise and novel definition for this notion and use twenty-nine data sets to show that the amount of observed error reduction is negatively correlated with the degree to which the descriptions make errors in a correlated manner. We empirically show that it is possible to learn descriptions that make less correlated errors in domains in which many ties in the search evaluation measure (e.g. information gain) are experienced during learning. The paper also presents results that help to understand when and why multiple descriptions are a help (irrelevant attributes) and when they are not as much help (large amounts of class noise).

Connected through hyperedge HE-1 (induced by source paper "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"):
  with members:
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Stochastically Guided Disjunctive Version Space Learning"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Learning Decision Lists Using Homogeneous Rules"
    - "Classifiers: A Theoretical and Empirical Study"

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