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: "On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)"
  Abstract: We consider the problem of fitting an n fi n distance matrix D by a tree metric T . Let " be the distance to the closest tree metric, that is, " = min T fk T; D k 1 g. First we present an O(n 2 ) algorithm for finding an additive tree T such that k T; D k 1 3", giving the first algorithm for this problem with a performance guarantee. Second we show that it is N P-hard to find a tree T such that k T; D k 1 &lt; 9

Connected through hyperedge HE-1 (induced by source paper "TREE CONTRACTIONS AND EVOLUTIONARY TREES"):
  with members:
    - "Efficient Algorithms for Inverting Evolution"

Connected through hyperedge HE-2 (induced by source paper "Efficient Algorithms for Inverting Evolution"):
  with members:
    - "TREE CONTRACTIONS AND EVOLUTIONARY TREES"
    - "Constructing Big Trees from Short Sequences"
    - "Learning Distributions from Random Walks"
    - "Design of Optimization Criteria for Multiple Sequence Alignment"

Connected through hyperedge HE-3 (induced by source paper "The Disk-Covering Method for Tree Reconstruction"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "A New Look at Tree Models for Multiple Sequence Alignment"):
  with members:
    - "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"

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