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: "VISIT: An Efficient Computational Model of Human Visual Attention"
  Abstract: One of the challenges for models of cognitive phenomena is the development of efficient and exible interfaces between low level sensory information and high level processes. For visual processing, researchers have long argued that an attentional mechanism is required to perform many of the tasks required by high level vision. This thesis presents VISIT, a connectionist model of covert visual attention that has been used as a vehicle for studying this interface. The model is efficient, exible, and is biologically plausible. The complexity of the network is linear in the number of pixels. Effective parallel strategies are used to minimize the number of iterations required. The resulting system is able to efficiently solve two tasks that are particularly difficult for standard bottom-up models of vision: computing spatial relations and visual search. Simulations show that the networks behavior matches much of the known psychophysical data on human visual attention. The general architecture of the model also closely matches the known physiological data on the human attention system. Various extensions to VISIT are discussed, including methods for learning the component modules.

Connected through hyperedge HE-1 (induced by source paper "Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification"):
  with members:
    - "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
    - "A Non-linear Information Maximisation Algorithm that Performs Blind Separation."
    - "Local Multivariate Binary Processors"
    - "Book Review New Kids on the Block way in the field of connectionist modeling. The"

Connected through hyperedge HE-2 (induced by source paper "Efficient Visual Search: A Connectionist Solution"):
  with members:
    - "Book Review New Kids on the Block way in the field of connectionist modeling. The"

Connected through hyperedge HE-3 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."):
  with members:
    - "Multiassociative Memory"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "A Delay-Line Based Motion Detection Chip"):
  with members:
    - "An Efficient Implementation of Sigmoidal Neural Nets in Temporal Coding with Noisy Spiking Neurons"
    - "Networks of Spiking Neurons: The Third Generation of Neural Network Models"

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