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: "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"
  Abstract: It is widely considered an ultimate connectionist objective to incorporate neural networks into intelligent systems. These systems are intended to possess a varied repertoire of functions enabling adaptable interaction with a non-static environment. The first step in this direction is to develop various neural network algorithms and models, the second step is to combine such networks into a modular structure that might be incorporated into a workable system. In this paper we consider one aspect of the second point, namely: processing reliability and hiding of wetware details. Pre- sented is an architecture for a type of neural expert module, named an Authority. An Authority consists of a number of Minos modules. Each of the Minos modules in an Authority has the same processing capabilities, but varies with respect to its particular specialization to aspects of the problem domain. The Authority employs the collection of Minoses like a panel of experts. The expert with the highest confidence is believed, and it is the answer and confidence quotient that are transmitted to other levels in a system hierarchy.

Connected through hyperedge HE-1 (induced by source paper "Submitted to Circuits, Systems and Signal Processing Neural Network Constructive Algorithms: Trading Generalization for Learning Efficiency?"):
  with members:
    - "Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity"
    - "Learning from Examples, Agent Teams and the Concept of Reflection"
    - "Rejection of Incorrect Answers from a Neural Net Classifier"

Connected through hyperedge HE-2 (induced by source paper "The Pandemonium System of Reflective Agents"):
  with members:
    - "Learning from Examples, Agent Teams and the Concept of Reflection"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"

Connected through hyperedge HE-3 (induced by source paper "Learning from Examples, Agent Teams and the Concept of Reflection"):
  with members:
    - "The Pandemonium System of Reflective Agents"
    - "Submitted to Circuits, Systems and Signal Processing Neural Network Constructive Algorithms: Trading Generalization for Learning Efficiency?"

Connected through hyperedge HE-4 (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"
    - "Neural Network Applicability: Classifying the Problem Space"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"
    - "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"

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