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: "Priority ASOCS  ASOCS models have two significant advantages over other learning models:"
  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. An ASOCS can operate in either a data processing mode or a learning mode. During data processing mode, an ASOCS acts as a parallel hardware circuit. During learning mode, an ASOCS incorporates a rule expressed as a Boolean conjunction in a distributed fashion in time logarithmic in the number of rules. This paper proposes a learning algorithm and architecture for Priority ASOCS. This new ASOCS model uses rules with priorities. The new model has significant learning time and space complexity improvements over previous models. Non-von Neumann architectures such as neural networks attack the word-at-a-time bottleneck of traditional computing systems [1]. Neural networks learn input-output mappings using highly distributed processing and memory [10,11,12]. Their numerous simple processing elements with modifiable weighted links permit a high degree of parallelism. A typical neural network has fixed topology. It learns by modifying weighted links between nodes. A new class of connectionist architectures has been proposed called ASOCS (Adaptive Self-Organizing Concurrent Systems) [4,5]. ASOCS models support efficient computation through self-organized learning and parallel execution. Learning is done through the incremental presentation of rules and/or examples. ASOCS models learn by modifying their topology. Data types include Boolean and multi-state variables; recent models support analog variables. The model incorporates rules into an adaptive logic network in a parallel and self organizing fashion. In processing mode, ASOCS supports fully parallel execution on actual inputs according to the learned rules. The adaptive logic network acts as a parallel hardware circuit during execution, mapping n input boolean vectors into m output boolean vectors, in a combinatoric fashion. The overall philosophy of ASOCS follows the high level goals of current neural network models. However, the mechanisms of learning and execution vary significantly. The ASOCS logic network is topologically dynamic with the network growing to efficiently fit the specific application. Current ASOCS models are based on digital nodes. ASOCS also supports use of symbolic and heuristic learning mechanisms, thus combining the parallelism and distributed nature of connectionist computing with the potential power of AI symbolic learning. A proof of concept ASOCS chip has been developed [2].

Connected through hyperedge HE-1 (induced by source paper "Automatic Feature Extraction in Machine Learning"):
  with members:
    - "A Self-Adjusting Dynamic Logic Module"
    - "BRACE: A Paradigm For the Discretization of Continuously Valued Data"

Connected through hyperedge HE-2 (induced by source paper "Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction"):
  with members:
    - "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based"
    - "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-Organizing Binary Decision Tree For Incrementally Defined Rule Based"
    - "A Self-Adjusting Dynamic Logic Module"
    - "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-Organizing Binary Decision Tree For Incrementally Defined Rule Based"
    - "A Self-Adjusting Dynamic Logic Module"
    - "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 "The Potential of Prototype Styles of Generalization"):
  with members:
    (none)

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