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: "Prosopagnosia in Modular Neural Network Models"
  Abstract: There is strong evidence that face processing in the brain is localized. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing other kinds of complex objects, indicates that face and non-face object recognition may be served by partially independent neural mechanisms. In this chapter, we use computational models to show how the face processing specialization apparently underlying prosopagnosia and visual object agnosia could be attributed to 1) a relatively simple competitive selection mechanism that, during development, devotes neural resources to the tasks they are best at performing, 2) the developing infant's need to perform subordinate classification (identification) of faces early on, and 3) the infant's low visual acuity at birth. Inspired by de Schonen and Mancini's (1998) arguments that factors like these could bias the visual system to develop a specialized face processor, and Jacobs and Kosslyn's (1994) experiments in the mixtures of experts (ME) modeling paradigm, we provide a preliminary computational demonstration of how this theory accounts for the double dissociation between face and object processing. We present two feed-forward computational models of visual processing. In both models, the selection mechanism is a gating network that mediates a competition between modules attempting to classify input stimuli. In Model I, when the modules are simple unbiased classifiers, the competition is sufficient to achieve enough of a specialization that damaging one module impairs the model's face recognition more than its object recognition, and damaging the other module impairs the model's object recognition more than its face recognition. In Model II, however, we bias the modules by providing one with low spatial frequency information and the other with high spatial frequency information. In this case, when the model's task is subordinate classification of faces and superordinate classification of objects, the low spatial frequency network shows an even stronger specialization for faces. No other combination of tasks and inputs shows this strong specialization. We take these results as support for the idea that something resembling a face processing "module" could arise as a natural consequence of the infant's developmental environment without being innately specified.

Connected through hyperedge HE-1 (induced by source paper "Task and Spatial Frequency Effects on Face Specialization"):
  with members:
    - "A Mixture of Experts Model Exhibiting Prosopagnosia"

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