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: "Maximum A Posteriori Classification of DNA Structure from Sequence Information"
  Abstract: We introduce an algorithm, lllama, which combines simple pattern recognizers into a general method for estimating the entropy of a sequence. Each pattern recognizer exploits a partial match between subsequences to build a model of the sequence. Since the primary features of interest in biological sequence domains are subsequences with small variations in exact composition, lllama is particularly suited to such domains. We describe two methods, lllama-length and lllama-alone, which use this entropy estimate to perform maximum a posteriori classification. We apply these methods to several problems in three-dimensional structure classification of short DNA sequences. The results include a surprisingly low 3.6% error rate in predicting helical conformation of oligonucleotides. We compare our results to those obtained using more traditional methods for automated generation of classifiers.

Connected through hyperedge HE-1 (induced by source paper "Feature Generation for Sequence Categorization"):
  with members:
    - "Transferring and Retraining Learned Information Filters"

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: Neural_Networks