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: "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"
  Abstract: Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Since SAT is NP-Complete, any other NP-Complete problem can be transformed into an equivalent SAT problem in polynomial time, and solved via either paradigm. This technique is illustrated for hamiltonian circuit (HC) problems.

Connected through hyperedge HE-1 (induced by source paper "A NN Algorithm for Boolean Satisfiability Problems"):
  with members:
    - "Simulated Annealing for Hard Satisfiability Problems"

Connected through hyperedge HE-2 (induced by source paper "Solving 3-SAT by GAs Adapting Constraint Weights"):
  with members:
    - "Genetic algorithms with multi-parent recombination"
    - "Graph Coloring with Adaptive Evolutionary Algorithms"

Connected through hyperedge HE-3 (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-4 (induced by source paper "A Comparative Study of Genetic Search"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"

Connected through hyperedge HE-5 (induced by source paper "Simulated Annealing for Hard Satisfiability Problems"):
  with members:
    - "Optimal Mutation Rates in Genetic Search"
    - "A NN Algorithm for Boolean Satisfiability Problems"

Connected through hyperedge HE-6 (induced by source paper "As mentioned in the introduction, an encod-ing/crossover pair makes a spectrum of geographical linkages. A"):
  with members:
    - "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"

Connected through hyperedge HE-7 (induced by source paper "A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms"):
  with members:
    - "The Application of a Parallel Genetic Algorithm to the n=m=P=C max Flowshop Problem"
    - "Genetic Algorithm based Scheduling in a Dynamic Manufacturing Environment"
    - "Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals"

Connected through hyperedge HE-8 (induced by source paper "Using Genetic Algorithms for Supervised Concept Learning"):
  with members:
    - "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification"
    - "Knowledge-Based Genetic Learning"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Adaptive Strategy Selection for Concept Learning"
    - "A Survey of Evolution Strategies"
    - "Is Consistency Harmful?"

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