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: "In  Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections"
  Abstract: Human vision systems integrate information nonlocally, across long spatial ranges. For example, a moving stimulus appears smeared when viewed briefly (30 ms), yet sharp when viewed for a longer exposure (100 ms) (Burr, 1980). This suggests that visual systems combine information along a trajectory that matches the motion of the stimulus. Our self-organizing neural network model shows how developmental exposure to moving stimuli can direct the formation of horizontal trajectory-specific motion integration pathways that unsmear representations of moving stimuli. These results account for Burr's data and can potentially also model other phenomena, such as visual inertia.

Connected through hyperedge HE-1 (induced by source paper "A Theory of Visual Relative Motion Perception: Grouping, Binding, and Gestalt Organization"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA"):
  with members:
    - "Modeling dynamic receptive field changes in primary visual cortex using inhibitory learning"
    - "Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning"
    - "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