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: "Constructing Big Trees from Short Sequences"
  Abstract: The construction of evolutionary trees is a fundamental problem in biology, and yet methods for reconstructing evolutionary trees are not reliable when it comes to inferring accurate topologies of large divergent evolutionary trees from realistic length sequences. We address this problem and present a new polynomial time algorithm for reconstructing evolutionary trees called the Short Quartets Method which is consistent and which has greater statistical power than other polynomial time methods, such as Neighbor-Joining and the 3-approximation algorithm by Agarwala et al. (and the "Double Pivot" variant of the Agarwala et al. algorithm by Cohen and Farach) for the L 1 -nearest tree problem. Our study indicates that our method will produce the correct topology from shorter sequences than can be guaranteed using these other methods.

Connected through hyperedge HE-1 (induced by source paper "Efficient Algorithms for Inverting Evolution"):
  with members:
    - "On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)"
    - "TREE CONTRACTIONS AND EVOLUTIONARY TREES"
    - "Learning Distributions from Random Walks"
    - "Design of Optimization Criteria for Multiple Sequence Alignment"

Connected through hyperedge HE-2 (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"
    - "The Disk-Covering Method for Tree Reconstruction"
    - "Efficient Algorithms for Inverting Evolution"

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