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 EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS"):
  Abstract: Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and often 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 distributed feedforward networks that use dynamic topologies (dynamic ANNs) efficiently in parallel hardware. 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 sup port for adding and deleting nodes dynamically during learning. In particular, this paper presents an LIT for standard Backpropagation with two layers of weights, and shows how dynamic extensions to Backpropagation can be supported.

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 NEURAL NETWORKS WITH LOCALIST PROPERTIES")
  Member "A VLSI Implementation of a Parallel, Self-Organizing Learning Model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES")
  Member "A Multi-Chip Module Implementation of a Neural Network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Priority ASOCS  ASOCS models have two significant advantages over other learning models:")
  Member "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2")
  Member "Growing Layers of Perceptrons: Introducing the Extentron Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2")

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