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: "Models of Parallel Adaptive Logic"
  Abstract: This paper overviews a proposed architecture for adaptive parallel logic referred to as ASOCS (Adaptive Self-Organizing Concurrent System). The ASOCS approach is based on an adaptive network composed of many simple computing elements which operate in a parallel asynchronous fashion. Problem specification is given to the system by presenting if-then rules in the form of boolean conjunctions. Rules are added incrementally and the system adapts to the changing rule-base. Adaptation and data processing form two separate phases of operation. During processing the system acts as a parallel hardware circuit. The adaptation process is distributed amongst the computing elements and efficiently exploits parallelism. Adaptation is done in a self-organizing fashion and takes place in time linear with the depth of the network. This paper summarizes the overall ASOCS concept and overviews three specific architectures.

Connected through hyperedge HE-1 (induced by source paper "ASOCS: A Multilayered Connectionist Network with Guaranteed Learning of Arbitrary Mappings"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."

Connected through hyperedge HE-2 (induced by source paper "DNA: A New ASOCS Model With Improved Implementation Potential"):
  with members:
    (none)

Connected through hyperedge HE-3 (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."
    - "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"

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