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: "Learning a Specialization for Face Recognition: The Effect of Spatial Frequency"
  Abstract: 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 mechanisms in the brain. Such a dissociation could be the result of a competitive learning mechanism that, during development, devotes neural resources to the tasks they are best at performing. Studies of normal adult performance on face and object recognition tasks seem to indicate that face recognition is primarily configural, involving the low spatial frequency information present in a stimulus over relatively large distances, whereas object recognition is primarily featural, involving analysis of the object's parts using local, high spatial frequency information. In a feed-forward computational model of visual processing, two modules compete to classify input stimuli; when one module receives low spatial frequency information and the other receives high spatial frequency information, the low-frequency module shows a strong specialization for face recognition in a combined face identification/object classification task. The series of experiments shows that the fine discrimination necessary for distinguishing members of a visually homoge neous class such as faces relies heavily on the low spatial frequencies present in a stimulus.

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

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