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 "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.

Includes members:
  Member "A Self-Adjusting Dynamic Logic Module":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Automatic Feature Extraction in Machine Learning")
  Member "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS")
  Member "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Growing Layers of Perceptrons: Introducing the Extentron Algorithm")
  Member "A VLSI Implementation of a Parallel, Self-Organizing Learning Model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS")

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