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 a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
  Abstract: Navigation through obstacles such as mine fields is an important capability for autonomous underwater vehicles. One way to produce robust behavior is to perform projective planning. However, real-time performance is a critical requirement in navigation. What is needed for a truly autonomous vehicle are robust reactive rules that perform well in a wide variety of situations, and that also achieve real-time performance. In this work, SAMUEL, a learning system based on genetic algorithms, is used to learn high-performance reactive strategies for navigation and collision avoidance.

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 "Learning Sequential Decision Rules Using Simulation Models and Competition"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Exploitation of Cooperation in Iterated Prisoner's Dilemma"
    - "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"
    - "Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"

Connected through hyperedge HE-3 (induced by source paper "Improving Tactical Plans with Genetic Algorithms"):
  with members:
    - "The Exploitation of Cooperation in Iterated Prisoner's Dilemma"
    - "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"
    - "Evaluation and Selection of Biases in Machine Learning"
    - "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"
    - "Explanations of Empirically Derived Reactive Plans"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"
    - "AN ENHANCER FOR REACTIVE PLANS"
    - "EVOLVING ROBOT BEHAVIORS"

Connected through hyperedge HE-4 (induced by source paper "EVOLVING ROBOT BEHAVIORS"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"

Connected through hyperedge HE-5 (induced by source paper "Explanations of Empirically Derived Reactive Plans"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "LEARNING CONCEPTS BY ASKING QUESTIONS"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"
    - "AN ENHANCER FOR REACTIVE PLANS"

Connected through hyperedge HE-6 (induced by source paper "ADAPTIVE TESTING OF CONTROLLERS FOR AUTONOMOUS VEHICLES"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"

Connected through hyperedge HE-7 (induced by source paper "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"):
  with members:
    - "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS"
    - "ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"
    - "AN ENHANCER FOR REACTIVE PLANS"

Connected through hyperedge HE-8 (induced by source paper "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"
    - "Explanations of Empirically Derived Reactive Plans"
    - "EVOLVING ROBOT BEHAVIORS"
    - "EVOLUTIONARY ALGORITHMS IN ROBOTICS"
    - "An Evolutionary Approach to Learning in Robots"

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