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: "Rapid learning of binding-match and binding-error detector circuits via long-term potentiation"
  Abstract: It is argued that the memorization of events and situations (episodic memory) requires the rapid formation of neural circuits responsive to binding errors and binding matches. While the formation of circuits responsive to binding matches can be modeled by associative learning mechanisms, the rapid formation of circuits responsive to binding errors is difficult to explain given their seemingly paradoxical behavior; such a circuit must be formed in response to the occurrence of a binding (i.e., a particular pattern in the input), but subsequent to its formation, it must not fire anymore in response to the occurrence of the very binding (i.e., pattern) that led to its formation. A plausible account of the formation of such circuits has not been offered. A computational model is described that demonstrates how a transient pattern of activity representing an event can lead to the rapid formation of circuits for detecting bindings and binding errors as a result of long-term potentiation within structures whose architecture and circuitry are similar to those of the hippocampal formation, a neural structure known to be critical to episodic memory. The model exhibits a high memory capacity and is robust against limited amounts of diffuse cell loss. The model also offers an alternate interpretation of the functional role of region CA3 in the formation of episodic memories, and predicts the nature of memory impairment that would result from damage to various regions of the hippocampal formation.

Connected through hyperedge HE-1 (induced by source paper "A Model of Rapid Memory Formation in the Hippocampal System"):
  with members:
    (none)

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