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: "Learning Sequential Decision Rules Using Simulation Models and Competition"
  Abstract: The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested.

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 "ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS"):
  with members:
    - "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"
    - "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"

Connected through hyperedge HE-3 (induced by source paper "EVOLVING ROBOT BEHAVIORS"):
  with members:
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "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-4 (induced by source paper "Learning to Sense Selectively in Physical Domains"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"

Connected through hyperedge HE-6 (induced by source paper "EVOLUTIONARY ALGORITHMS IN ROBOTICS"):
  with members:
    - "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-7 (induced by source paper "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"):
  with members:
    - "Robot Shaping: Developing Situated Agents through Learning"
    - "On the Relations Between Search and Evolutionary Algorithms"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "The Role of the Trainer in Reinforcement Learning"
    - "A Study of the Generalization Capabilities of XCS"
    - "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS"

Connected through hyperedge HE-8 (induced by source paper "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Improving Tactical Plans with Genetic Algorithms"
    - "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS"
    - "ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS"
    - "ADAPTIVE TESTING OF CONTROLLERS FOR AUTONOMOUS VEHICLES"

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