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: "A Multi-Chip Module Implementation of a Neural Network"
  Abstract: The requirement for dense interconnect in artificial neural network systems has led researchers to seek high-density interconnect technologies. This paper reports an implementation using multi-chip modules (MCMs) as the interconnect medium. The specific system described is a self-organizing, parallel, and dynamic learning model which requires a dense interconnect technology for effective implementation; this requirement is fulfilled by exploiting MCM technology. The ideas presented in this paper regarding an MCM implementation of artificial neural networks are versatile and can be adapted to apply to other neural network and connectionist models.

Connected through hyperedge HE-1 (induced by source paper "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS"):
  with members:
    - "A Self-Adjusting Dynamic Logic Module"
    - "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2"
    - "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES"
    - "Growing Layers of Perceptrons: Introducing the Extentron Algorithm"

Connected through hyperedge HE-2 (induced by source paper "Priority ASOCS  ASOCS models have two significant advantages over other learning models:"):
  with members:
    - "Automatic Feature Extraction in Machine Learning"
    - "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"
    - "The Potential of Prototype Styles of Generalization"

Connected through hyperedge HE-3 (induced by source paper "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"):
  with members:
    - "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"
    - "A Self-Adjusting Dynamic Logic Module"
    - "Priority ASOCS  ASOCS models have two significant advantages over other learning models:"
    - "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES"

Connected through hyperedge HE-4 (induced by source paper "A Self-Adjusting Dynamic Logic Module"):
  with members:
    - "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"
    - "Automatic Feature Extraction in Machine Learning"
    - "Using Multiple Node Types to Improve the Performance of DMP (Dynamic Multilayer Perceptron)"
    - "The Effect of Decision Surface Fitness on Dynamic Multilayer Perceptron Networks (DMP1)"
    - "Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction"
    - "Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2"
    - "A Provably Convergent Dynamic Training Method for Multilayer Perceptron Networks"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS"

Connected through hyperedge HE-5 (induced by source paper "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"):
  with members:
    - "Neural Network Applicability: Classifying the Problem Space"
    - "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"

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