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: "An Evolutionary Approach to Learning in Robots"
  Abstract: Evolutionary learning methods have been found to be useful in several areas in the development of intelligent robots. In the approach described here, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a way of reducing the overall knowledge engineering effort. This paper presents some initial results of applying the SAMUEL genetic learning system to a collision avoidance and navigation task for mobile robots.

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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "Explanations of Empirically Derived Reactive Plans"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Robot Shaping: Developing Situated Agents through Learning"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "The Role of the Trainer in Reinforcement Learning"
    - "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"
    - "EVOLVING ROBOT BEHAVIORS"
    - "EVOLUTIONARY ALGORITHMS IN ROBOTICS"

Connected through hyperedge HE-5 (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"
    - "Explanations of Empirically Derived Reactive Plans"
    - "EVOLVING ROBOT BEHAVIORS"
    - "EVOLUTIONARY ALGORITHMS IN ROBOTICS"

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