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: "Refining PID Controllers using Neural Networks"
  Abstract: The Kbann approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting the Manncon algorithm by which the mathematical equations governing a PID controller determine the topology and initial weights of a network, which is further trained using backpropagation. We apply this method to the task of controlling the outflow and temperature of a water tank, producing statistically-significant gains in accuracy over both a standard neural network approach and a non-learning PID controller. Furthermore, using the PID knowledge to initialize the weights of the network produces statistically less variation in testset accuracy when compared to networks initialized with small random numbers.

Connected through hyperedge HE-1 (induced by source paper "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"):
  with members:
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "Forward models: Supervised learning with a distal teacher"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"

Connected through hyperedge HE-2 (induced by source paper "The Neural Network House: An Overview"):
  with members:
    - "A comparison of neural net and conventional techniques for lighting control"

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: Neural_Networks