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 "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"):
  Abstract: We present a new self-organizing neural network model having two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization. The main advantage over existing approaches, e.g., the Kohonen feature map, is the ability of the model to automatically find a suitable network structure and size. This is achieved through a controlled growth process which also includes occasional removal of units. The second variant of the model is a supervised learning method which results from the combination of the abovementioned self-organizing network with the radial basis function (RBF) approach. In this model it is possible in contrast to earlier approaches toperform the positioning of the RBF units and the supervised training of the weights in parallel. Therefore, the current classification error can be used to determine where to insert new RBF units. This leads to small networks which generalize very well. Results on the two-spirals benchmark and a vowel classification problem are presented which are better than any results previously published. fl submitted for publication

Includes members:
  Member "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Soft Computing: the Convergence of Emerging Reasoning Technologies")
  Member "Introduction to Radial Basis Function Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,")
  Member "GROWING RADIAL BASIS FUNCTION NETWORKS":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning Controllers for Industrial Robots")
  Member "Scatter-partitioning RBF network for function regression and image":
    also connected through other hyperedges:
      (none)
  Member "Evolving Artificial Neural Networks using the Baldwin Effect":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Pruning Recurrent Neural Networks for Improved Generalization Performance")
  Member "LBG-U method for vector quantization an improvement over LBG inspired from neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature")
  Member "Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex.")

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: Neural_Networks