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: "Rearrangement of receptive field topography after intracortical and peripheral stimulation: The role of plasticity in"
  Abstract: Intracortical microstimulation (ICMS) of a single site in the somatosensory cortex of rats and monkeys for 2-6 hours produces a large increase in the number of neurons responsive to the skin region corresponding to the ICMS-site receptive field (RF), with very little effect on the position and size of the ICMS-site RF, and the response evoked at the ICMS site by tactile stimulation (Recanzone et al., 1992b). Large changes in RF topography are observed following several weeks of repetitive stimulation of a restricted skin region in monkeys (Jenkins et al., 1990; Recanzone et al., 1992acde). Repetitive stimulation of a localized skin region in monkeys produced by training the monkeys in a tactile frequency discrimination task improves their performance (Recanzone et al., 1992a). It has been suggested that these changes in RF topography are caused by competitive learning in excitatory pathways (Grajski & Merzenich, 1990; Jenkins et al., 1990; Recanzone et al., 1992abcde). ICMS almost simultaneously excites excitatory and inhibitory terminals and excitatory and inhibitory cortical neurons within a few microns of the stimulating electrode. Thus, this paper investigates the implications of the possibility that lateral inhibitory pathways too may undergo synaptic plasticity during ICMS. Lateral inhibitory pathways may also undergo synaptic plasticity in adult animals during peripheral conditioning. The "EXIN" (afferent excitatory and lateral inhibitory) synaptic plasticity rules

Connected through hyperedge HE-1 (induced by source paper "Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Using Sampling and Queries to Extract Rules from Trained Neural Networks"
    - "Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA"

Connected through hyperedge HE-2 (induced by source paper "Modeling dynamic receptive field changes in primary visual cortex using inhibitory learning"):
  with members:
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA"
    - "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