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: "Inserting the best known bounds for weighted bipar tite matching [11], with 1=2 p polynomial-time"
  Abstract: we apply the reduction to their two core children, the total sum of their matching weights becomes O(n), and if for each comparison of a spine node and a critical node we apply the reduction to the core child of the spine node, the total sum of their matching weights becomes O(n). With regards to the O( 2 ) comparisons of two critical nodes, their sum cannot exceed O( 2 n) in total weight. Thus, since we have a total of O(n) edges involved in the matchings, in time O(n), we can reduce the total sum of the matching weights to O( 2 n). Theorem 6.6 Let M : IR 1 ! IR be a monotone function bounding the time complexity UWBM. Moreover, let M satisfy that M (x) = x 1+" f(x), where " 0 is a constant, f (x) = O(x o(1) ), f is monotone, and for some constants b 1 ; b 2 , 8x; y b 1 : f(xy) b 2 f (x)f (y). Then, with = " p in time O(n 1+o(1) + M (n)). Proof: We spend O(n polylog n + time(UWBM(k 2 n))) on the matchings. So, by Theorem 5.10, we have that Comp-Core-Trees can be computed in time O(n polylog n + time(UWBM(k 2 n))). Applying Theo 4 = 16, we get Corollary 6.7 MAST is computable in time O(n 1:5 log n). [4] W.H.E. Day. Computational complexity of inferring phylogenies from dissimilarity matrices. Bulletin of Mathematical Biology, 49(4):461-467, 1987. [5] M. Farach, S. Kannan, and T. Warnow. A robust model for finding optimal evolutionary trees. Al-gorithmica, 1994. In press. See also STOC '93.

Connected through hyperedge HE-1 (induced by source paper "99-113. Construction of Phylogenetic Trees, Science, Fitting the Gene Lineage Into Its Species Lineage. A"):
  with members:
    - "A Six-Point Condition for Ordinal Matrices  keywords: additive, algorithm, evolution, ordinal, phylogeny"

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