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: "Transferring and Retraining Learned Information Filters"
  Abstract: Any system that learns how to filter documents will suffer poor performance during an initial training phase. One way of addressing this problem is to exploit filters learned by other users in a collaborative fashion. We investigate "direct transfer" of learned filters in this setting|a limiting case for any collaborative learning system. We evaluate the stability of several different learning methods under direct transfer, and conclude that symbolic learning methods that use negatively correlated features of the data perform poorly in transfer, even when they perform well in more conventional evaluation settings. This effect is robust: it holds for several learning methods, when a diverse set of users is used in training the classifier, and even when the learned classifiers can be adapted to the new user's distribution. Our experiments give rise to several concrete proposals for improving generalization performance in a collaborative setting, including a beneficial variation on a feature selection method that has been widely used in text categorization.

Connected through hyperedge HE-1 (induced by source paper "Is Learning The n-th Thing Any Easier Than Learning The First?"):
  with members:
    - "Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge"

Connected through hyperedge HE-2 (induced by source paper "Context-sensitive learning methods for text categorization"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Learning Trees and Rules with Set-valued Features"
    - "Mistake-Driven Learning in Text Categorization"
    - "Using and combining predictors that specialize"

Connected through hyperedge HE-3 (induced by source paper "Feature Generation for Sequence Categorization"):
  with members:
    - "Maximum A Posteriori Classification of DNA Structure from Sequence Information"

Connected through hyperedge HE-4 (induced by source paper "Incremental Reduced Error Pruning"):
  with members:
    - "More Efficient Windowing"

Connected through hyperedge HE-5 (induced by source paper "Learning Trees and Rules with Set-valued Features"):
  with members:
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Context-sensitive learning methods for text categorization"
    - "Inverse entailment and Progol"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"

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