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: "Growing Layers of Perceptrons: Introducing the Extentron Algorithm"
  Abstract: vations of perceptrons: (1) when the perceptron learning algorithm cycles among hyperplanes, the hyperplanes may be compared to select one that gives a best split of the examples, and (2) it is always possible for the perceptron to build a hyper- plane that separates at least one example from all the rest. We describe the Extentron which grows multi-layer networks capable of distinguishing non- linearly-separable data using the simple perceptron rule for linear threshold units. The resulting algorithm is simple, very fast, scales well to large prob - lems, retains the convergence properties of the perceptron, and can be completely specified using only two parameters. Results are presented comparing the Extentron to other neural network paradigms and to symbolic learning systems.

Connected through hyperedge HE-1 (induced by source paper "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS"):
  with members:
    - "A Self-Adjusting Dynamic Logic Module"
    - "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2"
    - "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"
    - "A Multi-Chip Module Implementation of a Neural Network"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES"

Connected through hyperedge HE-2 (induced by source paper "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2"):
  with members:
    - "A Self-Adjusting Dynamic Logic Module"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS"
    - "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES"

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