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: "The end of the line for a brain-damaged model of unilateral neglect"
  Abstract: For over a century, it has been known that damage to the right hemisphere of the brain can cause patients to be unaware of the contralesional side of space. This condition, known as unilateral neglect, represents a collection of clinically related spatial disorders characterized by the failure in free vision to respond, explore, or orient to stimuli predominantly located on the side of space opposite the damaged hemisphere. Recent studies using the simple task of line bisection, a conventional diagnostic test, have proved surprisingly revealing with respect to the spatial and attentional impairments involved in neglect. In line bisection, the patient is asked to mark the midpoint of a thin horizontal line on a sheet of paper. Neglect patients generally transect far to the right of the center. Extensive studies of line bisection have been conducted, manipulating|among other factors|line length, orientation, and position. We have simulated the pattern of results using an existing computational model of visual perception and selective attention called morsel (Mozer, 1991). morsel has already been used to model data in a related disorder, neglect dyslexia (Mozer & Behrmann, 1990). In this earlier work, morsel was "lesioned" in accordance with the damage we suppose to have occurred in the brains of

Connected through hyperedge HE-1 (induced by source paper "A Brief History of Connectionism"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Learning networks for face analysis and synthesis"
    - "Bayesian Unsupervised Learning of Higher Order Structure"
    - "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"

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