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: "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.

Connected through hyperedge HE-1 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."):
  with members:
    - "Multiassociative Memory"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"
    - "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"

Connected through hyperedge HE-2 (induced by source paper "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"):
  with members:
    - "A Self-Adjusting Dynamic Logic Module"
    - "Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction"
    - "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"
    - "A Generalizing Adaptive Discriminant Network"
    - "A Multi-Chip Module Implementation of a Neural Network"

Connected through hyperedge HE-3 (induced by source paper "Models of Parallel Adaptive Logic"):
  with members:
    - "ASOCS: A Multilayered Connectionist Network with Guaranteed Learning of Arbitrary Mappings"
    - "DNA: A New ASOCS Model With Improved Implementation Potential"
    - "Digital 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