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: "Modeling dynamic receptive field changes in primary visual cortex using inhibitory learning"
  Abstract: The position, size, and shape of the visual receptive field (RF) of some primary visual cortical neurons change dynamically, in response to artificial scotoma conditioning in cats (Pettet & Gilbert, 1992) and to retinal lesions in cats and monkeys (Darian-Smith & Gilbert, 1995). The "EXIN" learning rules (Marshall, 1995) are used to model dynamic RF changes. The EXIN model is compared with an adaptation model (Xing & Gerstein, 1994) and the LISSOM model (Sirosh & Miikkulainen, 1994; Sirosh et al., 1996). To emphasize the role of the lateral inhibitory learning rules, the EXIN and the LISSOM simulations were done with only lateral inhibitory learning. During scotoma conditioning, the EXIN model without feedforward learning produces centrifugal expansion of RFs initially inside the scotoma region, accompanied by increased responsiveness, without changes in spontaneous activation. The EXIN model without feedforward learning is more consistent with the neurophysiological data than are the adaptation model and the LISSOM model. The comparison between the EXIN and the LISSOM models suggests experiments to determine the role of feedforward excitatory and lateral inhibitory learning in producing dynamic RF changes during scotoma conditioning.

Connected through hyperedge HE-1 (induced by source paper "Modeling dynamic receptive field changes produced by intracortical microstimulation"):
  with members:
    - "Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA"

Connected through hyperedge HE-2 (induced by source paper "Rearrangement of receptive field topography after intracortical and peripheral stimulation: The role of plasticity in"):
  with members:
    - "Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning"

Connected through hyperedge HE-3 (induced by source paper "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"):
  with members:
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Self-Organization and Segmentation in a Laterally Connected Orientation Map of Spiking Neurons"
    - "A Neural Network Model of Visual Tilt Aftereffects"

Connected through hyperedge HE-4 (induced by source paper "Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA"):
  with members:
    - "Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning"
    - "In  Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections"
    - "Modeling dynamic receptive field changes produced by intracortical microstimulation"

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