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: "Multiassociative Memory"
  Abstract: This paper discusses the problem of how to implement many-to-many, or multi-associative, mappings within connectionist models. Traditional symbolic approaches wield explicit representation of all alternatives via stored links, or implicitly through enumerative algorithms. Classical pattern association models ignore the issue of generating multiple outputs for a single input pattern, and while recent research on recurrent networks is promising, the field has not clearly focused upon multi-associativity as a goal. In this paper, we define multiassociative memory MM, and several possible variants, and discuss its utility in general cognitive modeling. We extend sequential cascaded networks (Pollack 1987, 1990a) to fit the task, and perform several ini tial experiments which demonstrate the feasibility of the concept. This paper appears in The Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society. August 7-10, 1991.

Connected through hyperedge HE-1 (induced by source paper "Back Propagation is Sensitive to Initial Conditions"):
  with members:
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Scaling-up RAAMs"
    - "Replicability of Neural Computing Experiments"
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Neural Network Implementation in SAS R Software"

Connected through hyperedge HE-2 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."):
  with members:
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Neural Network Applicability: Classifying the Problem Space"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"
    - "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"

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