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: "TREE CONTRACTIONS AND EVOLUTIONARY TREES"
  Abstract: An evolutionary tree is a rooted tree where each internal vertex has at least two children and where the leaves are labeled with distinct symbols representing species. Evolutionary trees are useful for modeling the evolutionary history of species. An agreement subtree of two evolutionary trees is an evolutionary tree which is also a topological subtree of the two given trees. We give an algorithm to determine the largest possible number of leaves in any agreement subtree of two trees T 1 and T 2 with n leaves each. If the maximum degree d of these trees is bounded by a constant, the time complexity is O(n log 2 n) and is within a log n factor of optimal. For general d, this algorithm runs in O(nd 2 log d log 2 n) time or alternately in O(nd p d log 3 n) time.

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)"
    - "Constructing Big Trees from Short Sequences"
    - "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"
    - "The Disk-Covering Method for Tree Reconstruction"
    - "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: Neural_Networks