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 "Neural Network Applicability: Classifying the Problem Space"):
  Abstract: The tremendous current effort to propose neurally inspired methods of computation forces closer scrutiny of real world application potential of these models. This paper categorizes applications into classes and particularly discusses features of applications which make them efficiently amenable to neural network methods. Computational machines do deterministic mappings of inputs to outputs and many computational mechanisms have been proposed for problem solutions. Neural network features include parallel execution, adaptive learning, generalization, and fault tolerance. Often, much effort is given to a model and applications which can already be implemented in a much more efficient way with an alternate technology. Neural networks are potentially powerful devices for many classes of applications, but not all. However, it is proposed that the class of applications for which neural networks are efficient is both large and commonly occurring in nature. Comparison of supervised, unsupervised, and generalizing systems is also included.

Includes members:
  Member "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex.":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "Models of Parallel Adaptive Logic":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ASOCS: A Multilayered Connectionist Network with Guaranteed Learning of Arbitrary Mappings")
  Member "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction")

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