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: "Global self organization of all known protein sequences reveals inherent biological signatures self organization method"
  Abstract: A global classification of all currently known protein sequences is performed. Every protein sequence is partitioned into segments of 50 amino acids and a dynamic-programming distance is calculated between each pair of segments. This space of segments is first embedded into Euclidean space with small metric distortion. A novel self-organized cross-validated clustering algorithm is then applied to the embedded space with Euclidean distances. The resulting hierarchical tree of clusters offers a new representation of protein sequences and families, which compares favorably with the most updated classifications based on functional and structural protein data. Motifs and domains such as the Zinc Finger, EF hand, Homeobox, EGF-like and others are automatically correctly identified. A novel representation of protein families is introduced, from which functional biological kinship of protein families can be deduced, as demonstrated for the transporters family.

Connected through hyperedge HE-1 (induced by source paper "A map of the protein space An automatic hierarchical classification of all protein sequences"):
  with members:
    (none)

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