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: "Digital Neural Networks"
  Abstract: Demands for applications requiring massive parallelism in symbolic environments have given rebirth to research in models labeled as neura l networks. These models are made up of many simple nodes which are highly interconnected such that computation takes place as data flows amongst the nodes of the network. To present, most models have proposed nodes based on simple analog functions, where inputs are multiplied by weights and summed, the total then optionally being transformed by an arbitrary function at the node. Learning in these systems is accomplished by adjusting the weights on the input lines. This paper discusses the use of digital (boolean) nodes as a primitive building block in connectionist systems. Digital nodes naturally engender new paradigms and mechanisms for learning and processing in connectionist networks. The digital nodes are used as the basic building block of a class of models called ASOCS (Adaptive Self-Organizing Concurrent Systems). These models combine massive parallelism with the ability to adapt in a self-organizing fashion. Basic features of standard neural network learning algorithms and those proposed using digital nodes are compared and contrasted. The latter mechanisms can lead to vastly improved efficiency for many applications.

Connected through hyperedge HE-1 (induced by source paper "Models of Parallel Adaptive Logic"):
  with members:
    - "Neural Network Applicability: Classifying the Problem Space"
    - "ASOCS: A Multilayered Connectionist Network with Guaranteed Learning of Arbitrary Mappings"
    - "DNA: A New ASOCS Model With Improved Implementation Potential"

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