human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex.":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Local Multivariate Binary Processors")
  Member "Efficient Visual Search: A Connectionist Solution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Book Review New Kids on the Block way in the field of connectionist modeling. The")
  Member "A Delay-Line Based Motion Detection Chip":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Networks of Spiking Neurons: The Third Generation of Neural Network Models")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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