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: "Hill Climbing with Learning (An Abstraction of Genetic Algorithm)"
  Abstract: Simple modification of standard hill climbing optimization algorithm by taking into account learning features is discussed. Basic concept of this approach is the socalled probability vector, its single entries determine probabilities of appearance of '1' entries in n-bit vectors. This vector is used for the random generation of n-bit vectors that form a neighborhood (specified by the given probability vector). Within the neighborhood a few best solutions (with smallest functional values of a minimized function) are recorded. The feature of learning is introduced here so that the probability vector is updated by a formal analogue of Hebbian learning rule, well-known in the theory of artificial neural networks. The process is repeated until the probability vector entries are close either to zero or to one. The resulting probability vector unambiguously determines an n-bit vector which may be interpreted as an optimal solution of the given optimization task. Resemblance with genetic algorithms is discussed. Effectiveness of the proposed method is illustrated by an example of looking for global minima of a highly multimodal function.

Connected through hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

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"
    - "MIMIC: Finding Optima by Estimating Probability Densities"

Connected through hyperedge HE-3 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"

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