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: "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"
  Abstract: The Building-Block Hypothesis appeals to the notion of problem decomposition and the assembly of solutions from sub-solutions. Accordingly, there have been many varieties of GA test problems with a structure based on building-blocks. Many of these problems use deceptive fitness functions to model interdependency between the bits within a block. However, very few have any model of interdependency between building-blocks; those that do are not consistent in the type of interaction used intra-block and inter-block. This paper discusses the inadequacies of the various test problems in the literature and clarifies the concept of building-block interdependency. We formulate a principled model of hierarchical interdependency that can be applied through many levels in a consistent manner and introduce Hierarchical If-and-only-if (H-IFF) as a canonical example. We present some empirical results of GAs on H-IFF showing that if population diversity is maintained and linkage is tight then the GA is able to identify and manipulate building-blocks over many levels of assembly, as the Building-Block Hypothesis suggests.

Connected through hyperedge HE-1 (induced by source paper "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem"
    - "Voting for Schemata"
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "A comparison of the fixed and floating building block representation in the genetic algorithm"
    - "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."
    - "The Impact of External Dependency in Genetic Programming Primitives"

Connected through hyperedge HE-2 (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-3 (induced by source paper "The Schema Theorem and Price's Theorem"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"
    - "Evolution in Time and Space The Parallel Genetic Algorithm"
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"
    - "Price's Theorem and the MAX Problem"

Connected through hyperedge HE-4 (induced by source paper "When Will a Genetic Algorithm Outperform Hill Climbing?"):
  with members:
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"

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