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 Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"
  Abstract: This paper presents an ASOCS (adaptive self-organizing concurrent system) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on adaptive algorithm 3 (AA3) and details its architecture and learning algorithm. It has advantages over previous ASOCS models in simplicity, implementability, and cost. An ASOCS can operate in either a data processing mode or a learning mode. During the data processing mode, an ASOCS acts as a parallel hardware circuit. In learning mode, rules expressed as boolean conjunctions are incrementally presented to the ASOCS. All ASOCS learning algorithms incorporate a new rule in a distributed fashion in a short, bounded time.

Connected through hyperedge HE-1 (induced by source paper "Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction"):
  with members:
    - "A Self-Adjusting Dynamic Logic Module"
    - "Priority ASOCS  ASOCS models have two significant advantages over other learning models:"

Connected through hyperedge HE-2 (induced by source paper "A Generalizing Adaptive Discriminant Network"):
  with members:
    - "A Self-Adjusting Dynamic Logic Module"

Connected through hyperedge HE-3 (induced by source paper "A Multi-Chip Module Implementation of a Neural Network"):
  with members:
    - "A Self-Adjusting Dynamic Logic Module"
    - "Priority ASOCS  ASOCS models have two significant advantages over other learning models:"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS"
    - "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"

Connected through hyperedge HE-4 (induced by source paper "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"):
  with members:
    - "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"
    - "A Multi-Chip Module Implementation of a Neural Network"
    - "Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES"

Connected through hyperedge HE-5 (induced by source paper "A Self-Adjusting Dynamic Logic Module"):
  with members:
    - "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"
    - "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"

Connected through hyperedge HE-6 (induced by source paper "Neural Network Applicability: Classifying the Problem Space"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Models of Parallel Adaptive Logic"

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