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: "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES"
  Abstract: Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and typically suffer from a number of shortcomings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing feedforward networks that use dynamic topologies. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents LITs for the single-layer competitve learning network, and the counterpropagation network, which combines elements of supervised learning with competitive learning. These two networks are localist in the sense that ultimately one node is responsible for each output. LITs for other models are presented in other papers.

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"
    - "Growing Layers of Perceptrons: Introducing the Extentron Algorithm"

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"
    - "Growing Layers of Perceptrons: Introducing the Extentron Algorithm"

Connected through hyperedge HE-3 (induced by source paper "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"):
  with members:
    - "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"
    - "A Self-Adjusting Dynamic Logic Module"
    - "Priority ASOCS  ASOCS models have two significant advantages over other learning models:"
    - "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS"
    - "A Multi-Chip Module Implementation of a Neural Network"

Connected through hyperedge HE-4 (induced by source paper "A Self-Adjusting Dynamic Logic Module"):
  with members:
    - "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"
    - "Automatic Feature Extraction in Machine Learning"
    - "Using Multiple Node Types to Improve the Performance of DMP (Dynamic Multilayer Perceptron)"
    - "The Effect of Decision Surface Fitness on Dynamic Multilayer Perceptron Networks (DMP1)"
    - "Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction"
    - "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2"
    - "A Provably Convergent Dynamic Training Method for Multilayer Perceptron Networks"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION 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