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: "Fitness Landscapes and Difficulty in Genetic Programming"
  Abstract: The structure of the fitness landscape on which genetic programming operates is examined. The landscapes of a range of problems of known difficulty are analyzed in an attempt to determine which landscape measures correlate with the difficulty of the problem. The autocorrelation of the fitness values of random walks, a measure which has been shown to be related to perceived difficulty using other techniques, is only a weak indicator of the difficulty as perceived by genetic programming. All of these problems show unusually low autocorrelation. Comparison of the range of landscape basin depths at the end of adaptive walks on the landscapes shows good correlation with problem difficulty, over the entire range of problems examined.

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 "A Simulation of Adaptive Agents in a Hostile Environment"):
  with members:
    - "Strongly Typed Genetic Programming"
    - "Evolving Cooperation Strategies"

Connected through hyperedge HE-3 (induced by source paper "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"):
  with members:
    - "Dynamic Parameter Encoding for Genetic Algorithms"
    - "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies"
    - "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"

Connected through hyperedge HE-4 (induced by source paper "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies"):
  with members:
    - "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"
    - "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"

Connected through hyperedge HE-5 (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"
    - "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"
    - "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-6 (induced by source paper "Genetic Programming and Redundancy"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Causality in Genetic Programming"
    - "Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, Fitness Causes Bloat in"
    - "An Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth of Crossover"
    - "The MAX Problem for Genetic Programming Highlighting an Adverse Interaction between the Crossover Operator and"

Connected through hyperedge HE-7 (induced by source paper "Effects of Occam's Razor in Evolving Sigma-Pi Neural Nets"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic Programming of Minimal Neural Nets Using Occam's Razor"
    - "Evolving Optimal Neural Networks Using Genetic Algorithms with Occam's Razor"

Connected through hyperedge HE-8 (induced by source paper "Complexity Compression and Evolution"):
  with members:
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Signal Path Oriented Approach for Generation of Dynamic Process Models"
    - "Culling Teaching -1 Culling and Teaching in Neuro-evolution"
    - "A Study in Program Response and the Negative Effects of Introns in Genetic Programming"
    - "A Survey of Intron Research in Genetics"

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