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: "A Theory of Visual Relative Motion Perception: Grouping, Binding, and Gestalt Organization"
  Abstract: The human visual system is more sensitive to the relative motion of objects than to their absolute motion. An understanding of motion perception requires an understanding of how neural circuits can group moving visual elements relative to one another, based upon hierarchical reference frames. We have modeled visual relative motion perception using a neural network architecture that groups visual elements according to Gestalt common-fate principles and exploits information about the behavior of each group to predict the behavior of individual elements. A simple competitive neural circuit binds visual elements together into a representation of a visual object. Information about the spiking pattern of neurons allows transfer of the bindings of an object representation from location to location in the neural circuit as the object moves. The model exhibits characteristics of human object grouping and solves some key neural circuit design problems in visual relative motion perception.

Connected through hyperedge HE-1 (induced by source paper "In  Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections"):
  with members:
    - "Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA"

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