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 Search Of Articulated Attractors"
  Abstract: Recurrent attractor networks offer many advantages over feed-forward networks for the modeling of psychological phenomena. Their dynamic nature allows them to capture the time course of cognitive processing, and their learned weights may often be easily interpreted as soft constraints between representational components. Perhaps the most significant feature of such networks, however, is their ability to facilitate generalization by enforcing well formedness constraints on intermediate and output representations. Attractor networks which learn the systematic regularities of well formed representations by exposure to a small number of examples are said to possess articulated attractors. This paper investigates the conditions under which articulated attractors arise in recurrent networks trained using variants of backpropagation. The results of computational experiments demonstrate that such structured attrac-tors can spontaneously appear in an emergence of systematic-ity, if an appropriate error signal is presented directly to the recurrent processing elements. We show, however, that distal error signals, backpropagated through intervening weights, pose serious problems for networks of this kind. We present simulation results, discuss the reasons for this difficulty, and suggest some directions for future attempts to surmount it.

Connected through hyperedge HE-1 (induced by source paper "On the Computational Utility of Consciousness"):
  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