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: "Hidden Markov Modeling of simultaneously recorded cells in the Associative cortex of behaving monkeys"
  Abstract: A widely held idea regarding information processing in the brain is the cell-assembly hypothesis suggested by Hebb in 1949. According to this hypothesis, the basic unit of information processing in the brain is an assembly of cells, which can act briefly as a closed system, in response to a specific stimulus. This work presents a novel method of characterizing this supposed activity using a Hidden Markov Model. This model is able to reveal some of the underlying cortical network activity of behavioral processes. In our study the process in hand was the simultaneous activity of several cells recorded from the frontal cortex of behaving monkeys. Using such a model we were able to identify the behavioral mode of the animal and directly identify the corresponding collective network activity. Furthermore, the segmentation of the data into the discrete states also provides direct evidence for the state dependency of the short-time correlation functions between the same pair of cells. Thus, this cross-correlation depends on the network state of activity and not on local connectivity alone.

Connected through hyperedge HE-1 (induced by source paper "Cortical activity flips among quasi stationary states"):
  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