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: "Evolving Artificial Neural Networks using the Baldwin Effect"
  Abstract: This paper describes how through simple means a genetic search towards optimal neural network architectures can be improved, both in the convergence speed as in the quality of the final result. This result can be theoretically explained with the Baldwin effect, which is implemented here not just by the learning process of the network alone, but also by changing the network architecture as part of the learning procedure. This can be seen as a combination of two different techniques, both help ing and improving on simple genetic search.

Connected through hyperedge HE-1 (induced by source paper "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Introduction to Radial Basis Function Networks"
    - "GROWING RADIAL BASIS FUNCTION NETWORKS"
    - "Scatter-partitioning RBF network for function regression and image"
    - "LBG-U method for vector quantization an improvement over LBG inspired from neural networks"
    - "Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map"

Connected through hyperedge HE-2 (induced by source paper "Pruning Recurrent Neural Networks for Improved Generalization Performance"):
  with members:
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Extraction of Rules from Discrete-Time Recurrent Neural Networks"
    - "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

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