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: "Efficient Algorithms for Inverting Evolution"
  Abstract: Evolution is a stochastic process which operates on the DNA of species. The evolutionary process leaves tell-tale signs in the DNA which can be used to construct phylogenies, or evolutionary trees, for a set of species. Maximum Likelihood Estimations (MLE) methods seek the evolutionary tree which is most likely to have produced the DNA under consideration. While these methods are widely accepted and intellectually satisfying, they have been computationally intractable. In this paper, we address the intractability of MLE methods as follows. We introduce a metric on stochastic process models of evolution. We show that this metric is meaningful by proving that in order for any algorithm to distinguish between two stochatic models that are close according to this metric, it needs to be given a lot of observations. We complement this result with a simple and efficient algorithm for inverting the stochastic process of evolution, that is, for building the tree from observations on the DNA of the species. Our result can be viewed as a result on the PAC-learnability of the class of distributions produced by tree-like processes. Though there have been many heuristics suggested for this problem, our algorithm is the first one with a guaranteed convergence rate, and further, this rate is within a polynomial of the lower-bound rate we establish. Ours is also the the first polynomial-time algorithm which is guaranteed to converge at all to the correct tree.

Connected through hyperedge HE-1 (induced by source paper "TREE CONTRACTIONS AND EVOLUTIONARY TREES"):
  with members:
    - "On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)"

Connected through hyperedge HE-2 (induced by source paper "Learning Distributions from Random Walks"):
  with members:
    - "Applying Winnow to Context-Sensitive Spelling Correction"

Connected through hyperedge HE-3 (induced by source paper "Design of Optimization Criteria for Multiple Sequence Alignment"):
  with members:
    (none)

Connected through hyperedge HE-4 (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"
    - "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