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: "Regularities in a Random Mapping from Orthography to Semantics"
  Abstract: In this paper we investigate representational and methodological issues in a attractor network model of the mapping from orthography to semantics based on [Plaut, 1995]. We find that, contrary to psycholinguistic studies, the response time to concrete words (represented by more 1 bits in the output pattern) is slower than for abstract words. This model also predicts that response times to words in a dense semantic neighborhood will be faster than words which have few semantically similar neighbors in the language. This is conceptually consistent with the neighborhood effect seen in the mapping from orthography to phonology [Seidenberg & McClelland, 1989, Plaut et al., 1996] in that patterns with many neighbors are faster in both pathways, but since there is no regularity in the random mapping used here, it is clear that the cause of this effect is different than that of previous experiments. We also report a rather distressing finding. Reaction time in this model is measured by the time it takes the network to settle after being presented with a new input. When the criterion used to determine when the network is settled is changed to include testing of the hidden units, each of the results reported above change the direction of effect abstract words are now slower, as are words in dense semantic neighborhoods. Since there are independent reasons to exclude hidden units from the stopping criterion, and this is what is done in common practice, we believe this phenomenon to be of interest mostly to neural network practitioners. However, it does provide some insight into the interaction between the hidden and output units during settling.

Connected through hyperedge HE-1 (induced by source paper "Acquiring the mapping from meaning to sounds"):
  with members:
    - "Forward models: Supervised learning with a distal teacher"

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