human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Real-time Interactive Neuro-evolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Evolving Neural Networks to Play Go")
  Member "Evolving Obstacle Avoidance Behavior in a Robot Arm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "2-D Pole Balancing with Recurrent Evolutionary Networks")
  Member "Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "2-D Pole Balancing with Recurrent Evolutionary Networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Evolving Obstacle Avoidance Behavior in a Robot Arm")
  Member "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME")
  Member "A Coevolutionary Approach to Learning Sequential Decision Rules":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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