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: "Feature Generation for Sequence Categorization"
  Abstract: The problem of sequence categorization is to generalize from a corpus of labeled sequences procedures for accurately labeling future unlabeled sequences. The choice of representation of sequences can have a major impact on this task, and in the absence of background knowledge a good representation is often not known and straightforward representations are often far from optimal. We propose a feature generation method (called FGEN) that creates Boolean features that check for the presence or absence of heuristically selected collections of subsequences. We show empirically that the representation computed by FGEN improves the accuracy of two commonly used learning systems (C4.5 and Ripper) when the new features are added to existing representations of sequence data. We show the superiority of FGEN across a range of tasks selected from three domains: DNA sequences, Unix command sequences, and English text.

Connected through hyperedge HE-1 (induced by source paper "Maximum A Posteriori Classification of DNA Structure from Sequence Information"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Transferring and Retraining Learned Information Filters"):
  with members:
    - "Incremental Reduced Error Pruning"
    - "Learning Trees and Rules with Set-valued Features"
    - "Context-sensitive learning methods for text categorization"
    - "Is Learning The n-th Thing Any Easier Than Learning The First?"

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