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: "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
  Abstract: This article presents a new reinforcement learning method called SANE (Symbiotic, Adaptive Neuro-Evolution), which evolves a population of neurons through genetic algorithms to form a neural network capable of performing a task. Symbiotic evolution promotes both cooperation and specialization, which results in a fast, efficient genetic search and discourages convergence to suboptimal solutions. In the inverted pendulum problem, SANE formed effective networks 9 to 16 times faster than the Adaptive Heuristic Critic and 2 times faster than Q-learning and the GENITOR neuro-evolution approach without loss of generalization. Such efficient learning, combined with few domain assumptions, make SANE a promising approach to a broad range of reinforcement learning problems, including many real-world applications.

Connected through hyperedge HE-1 (induced by source paper "2-D Pole Balancing with Recurrent Evolutionary Networks"):
  with members:
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "Incremental Evolution of Complex General Behavior"

Connected through hyperedge HE-2 (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."
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"

Connected through hyperedge HE-3 (induced by source paper "Real-time Interactive Neuro-evolution"):
  with members:
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "Incremental Evolution of Complex General Behavior"
    - "Evolving Neural Networks to Play Go"

Connected through hyperedge HE-4 (induced by source paper "Evolving Obstacle Avoidance Behavior in a Robot Arm"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Hierarchical Evolution of Neural Networks"
    - "Issues in Evolutionary Robotics"
    - "2-D Pole Balancing with Recurrent Evolutionary Networks"

Connected through hyperedge HE-5 (induced by source paper "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"):
  with members:
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "A Cooperative Coevolutionary Approach to Function Optimization"

Connected through hyperedge HE-6 (induced by source paper "A Coevolutionary Approach to Learning Sequential Decision Rules"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Knowledge-Based Genetic Learning"
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Automatic Modularization by Speciation"
    - "Evolving Complex Structures via Co- operative Coevolution"

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: Reinforcement_Learning