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: "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"
  Abstract: Over the years there has been several packages developed that provide a workbench for genetic algorithm (GA) research. Most of these packages use the generational model inspired by GENESIS. A few have adopted the steady-state model used in Genitor. Unfortunately, they have some deficiencies when working with order-based problems such as packing, routing, and scheduling. This paper describes LibGA, which was developed specifically for order-based problems, but which also works easily with other kinds of problems. It offers an easy to use `user-friendly' interface and allows comparisons to be made between both generational and steady-state genetic algorithms for a particular problem. It includes a variety of genetic operators for reproduction, crossover, and mutation. LibGA makes it easy to use these operators in new ways for particular applications or to develop and include new operators. Finally, it offers the unique new feature of a dynamic generation gap.

Connected through hyperedge HE-1 (induced by source paper "Heuristic for Improved Genetic Bin Packing"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."

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 "A GENETIC ALGORITHM FOR FRAGMENT ALLOCATION IN A DISTRIBUTED DATABASE SYSTEM"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Genetic Algorithm for File and Task Placement in a Distributed System"

Connected through hyperedge HE-4 (induced by source paper "Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A genetic prototype learner"
    - "Using Genetic Algorithms for Supervised Concept Learning"

Connected through hyperedge HE-5 (induced by source paper "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic algorithms with multi-parent recombination"
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"

Connected through hyperedge HE-6 (induced by source paper "A Genetic Algorithm for File and Task Placement in a Distributed System"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A GENETIC ALGORITHM FOR FRAGMENT ALLOCATION IN A DISTRIBUTED DATABASE SYSTEM"

Connected through hyperedge HE-7 (induced by source paper "Case-Based Probability Factoring in Bayesian Belief Networks"):
  with members:
    - "Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Decision-Theoretic Case-Based Reasoning"

Connected through hyperedge HE-8 (induced by source paper "Genetic algorithms with multi-parent recombination"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "Multi-parent Recombination"
    - "Graph Coloring with Adaptive Evolutionary Algorithms"
    - "Solving 3-SAT by GAs Adapting Constraint Weights"
    - "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"

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