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: "Simulating Quadratic Dynamical Systems is PSPACE-complete (preliminary version)"
  Abstract: Quadratic Dynamical Systems (QDS), whose definition extends that of Markov chains, are used to model phenomena in a variety of fields like statistical physics and natural evolution. Such systems also play a role in genetic algorithms, a widely-used class of heuristics that are notoriously hard to analyze. Recently Rabinovich et al. took an important step in the study of QDS's by showing, under some technical assumptions, that such systems converge to a stationary distribution (similar theorems for Markov Chains are well-known). We show, however, that the following sampling problem for QDS's is PSPACE-hard: Given an initial distribution, produce a random sample from the t'th generation. The hardness result continues to hold for very restricted classes of QDS's with very simple initial distributions, thus suggesting that QDS's are intrinsically more complicated than Markov chains.

Connected through hyperedge HE-1 (induced by source paper "A Computational View of Population Genetics (preliminary version)"):
  with members:
    - "Where Genetic Algorithms Excel"

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: Genetic_Algorithms