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 "Models of perceptual learning in vernier hyperacuity"):
  Abstract: Performance of human subjects in a wide variety of early visual processing tasks improves with practice. HyperBF networks (Poggio and Girosi, 1990) constitute a mathematically well-founded framework for understanding such improvement in performance, or perceptual learning, in the class of tasks known as visual hyperacuity. The present article concentrates on two issues raised by the recent psychophysical and computational findings reported in (Poggio et al., 1992b; Fahle and Edelman, 1992). First, we develop a biologically plausible extension of the HyperBF model that takes into account basic features of the functional architecture of early vision. Second, we explore various learning modes that can coexist within the HyperBF framework and focus on two unsupervised learning rules which may be involved in hyperacuity learning. Finally, we report results of psychophysical experiments that are consistent with the hypothesis that activity-dependent presynaptic amplification may be involved in perceptual learning in hyperacuity.

Includes members:
  Member "Learning networks for face analysis and synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Receptive Fields for Vision: from Hyperacuity to Object Recognition")
  Member "An integrated approach to the study of object features in visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Feature Extraction Using an Unsupervised Neural Network")
  Member "Receptive Fields for Vision: from Hyperacuity to Object Recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning networks for face analysis and synthesis")

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