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: "MIMIC: Finding Optima by Estimating Probability Densities"
  Abstract: In many optimization problems, the structure of solutions reflects complex relationships between the different input parameters. For example, experience may tell us that certain parameters are closely related and should not be explored independently. Similarly, experience may establish that a subset of parameters must take on particular values. Any search of the cost landscape should take advantage of these relationships. We present MIMIC, a framework in which we analyze the global structure of the optimization landscape. A novel and efficient algorithm for the estimation of this structure is derived. We use knowledge of this structure to guide a randomized search through the solution space and, in turn, to refine our estimate of the structure. Our technique obtains significant speed gains over other randomized optimization procedures.

Connected through hyperedge HE-1 (induced by source paper "Where Genetic Algorithms Excel"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "A Computational View of Population Genetics (preliminary version)"

Connected through hyperedge HE-2 (induced by source paper "Fast Probabilistic Modeling for Combinatorial Optimization"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Hill Climbing with Learning (An Abstraction of Genetic Algorithm)"

Connected through hyperedge HE-3 (induced by source paper "Reinforcement Learning by Probability Matching"):
  with members:
    (none)

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