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: "EVOLUTIONARY ALGORITHMS IN ROBOTICS"

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 "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"
    - "An Evolutionary Approach to Learning in Robots"

Connected through hyperedge HE-4 (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"
    - "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