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: "The Disk-Covering Method for Tree Reconstruction"
  Abstract: Evolutionary tree reconstruction is a very important step in many biological research problems, and yet is extremely difficult for a variety of computational, statistical, and scientific reasons. In particular, the reconstruction of very large trees containing significant amounts of divergence is especially challenging. We present in this paper a new tree reconstruction method, which we call the Disk-Covering Method, which can be used to recover accurate estimations of the evolutionary tree for otherwise intractable datasets. DCM obtains a decomposition of the input dataset into small overlapping sets of closely related taxa, reconstructs trees on these subsets (using a "base" phylogenetic method of choice), and then combines the subtrees into one tree on the entire set of taxa. Because the subproblems analyzed by DCM are smaller, com-putationally expensive methods such as maximum likelihood estimation can be used without incurring too much cost. At the same time, because the taxa within each subset are closely related, even very simple methods (such as neighbor-joining) are much more likely to be highly accurate. The result is that DCM-boosted methods are typically faster and more accurate as compared to "naive" use of the same method. In this paper we describe the basic ideas and techniques in DCM, and demonstrate the advantages of DCM experimentally by simulating sequence evolution on a variety of trees.

Connected through hyperedge HE-1 (induced by source paper "On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)"):
  with members:
    - "A New Look at Tree Models for Multiple Sequence Alignment"
    - "TREE CONTRACTIONS AND EVOLUTIONARY TREES"
    - "Efficient Algorithms for Inverting Evolution"
    - "Constructing Big Trees from Short Sequences"

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