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: "Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction"
  Abstract: Different learning models employ different styles of generalization on novel inputs. This paper proposes the need for multiple styles of generalization to support a broad application base. The Priority ASOCS model (Priority Adaptive Self-Organizing Concurrent System) is overviewed and presented as a potential platform which can support multiple generalization styles. PASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. The PASOCS can operate in either a data processing mode or a learning mode. During data processing mode, the system acts as a parallel hardware circuit. During learning mode, the PASOCS incorporates rules, with attached priorities, which represent the application being learned. Learning is accomplished in a distributed fashion in time logarithmic in the number of rules. The new model has significant learning time and space complexity improvements over previous models. Generalization in a learning system is at best always a guess. The proper style of generalization is application dependent. Thus, one style of generalization may not be sufficient to allow a learning system to support a broad spectrum of applications [14]. Current connectionist models use one specific style of generalization which is implicit in the learning algorithm. We suggest that the type of generalization used be a self-organizing parameter of the learning system which can be discovered as learning takes place. This requires a) a model which allows flexible generalization styles, and b) mechanisms to guide the system into the best style of generalization for the problem being learned. This paper overviews a learning model which seeks to efficiently support requirement a) above. The model is called Priority ASOCS (PASOCS) [9], which is a member of a class of models called ASOCS (Adaptive Self-Organizing Concurrent Systems) [5]. Section 2 of this paper gives an example of how different generalization techniques can approach a problem. Section 3 presents an overview of PASOCS. Section 4 illustrates how flexible generalization can be supported. Section 5 concludes the paper.

Connected through hyperedge HE-1 (induced by source paper "Priority ASOCS  ASOCS models have two significant advantages over other learning models:"):
  with members:
    - "Automatic Feature Extraction in Machine Learning"
    - "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"
    - "A Multi-Chip Module Implementation of a Neural Network"
    - "The Potential of Prototype Styles of Generalization"

Connected through hyperedge HE-2 (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)"
    - "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-3 (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"
    - "A VLSI Implementation of a Parallel, Self-Organizing Learning Model"
    - "A Generalizing Adaptive Discriminant Network"
    - "A Multi-Chip Module Implementation of a Neural 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