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: "Explanations of Empirically Derived Reactive Plans"
  Abstract: Given an adequate simulation model of the task environment and payoff function that measures the quality of partially successful plans, competition-based heuristics such as genetic algorithms can develop high performance reactive rules for interesting sequential decision tasks. We have previously described an implemented system, called SAMUEL, for learning reactive plans and have shown that the system can successfully learn rules for a laboratory scale tactical problem. In this paper, we describe a method for deriving explanations to justify the success of such empirically derived rule sets. The method consists of inferring plausible subgoals and then explaining how the reactive rules trigger a sequence of actions (i.e., a stra tegy) to satisfy the subgoals.

Connected through hyperedge HE-1 (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"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"

Connected through hyperedge HE-2 (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"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "Evaluation and Selection of Biases in Machine Learning"
    - "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"
    - "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-3 (induced by source paper "LEARNING CONCEPTS BY ASKING QUESTIONS"):
  with members:
    - "Inductive Logic Programming"
    - "Automated Refinement of First-Order Horn-Clause Domain Theories"
    - "Relating Relational Learning Algorithms"
    - "Acquiring Recursive and Iterative Concepts with Explanation-Based Learning explanation-based generalization, generalizing explanation structures, generalizing to"
    - "Observation and Generalisation in a Simulated Robot World"
    - "Learning First-Order Acyclic Horn Programs from Entailment"
    - "The Origins of Inductive Logic Programming: A Prehistoric Tale"

Connected through hyperedge HE-4 (induced by source paper "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"
    - "EVOLVING ROBOT BEHAVIORS"
    - "ADAPTIVE TESTING OF CONTROLLERS FOR AUTONOMOUS VEHICLES"
    - "An Evolutionary Approach to Learning in Robots"

Connected through hyperedge HE-5 (induced by source paper "AN ENHANCER FOR REACTIVE PLANS"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"

Connected through hyperedge HE-6 (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"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS"
    - "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