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: "JUNG ET AL.: ESTIMATING ALERTNESS FORM THE EEG POWER SPECTRUM 1 Estimating Alertness from the"
  Abstract: In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, we show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEG measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.

Connected through hyperedge HE-1 (induced by source paper "Independent Component Analysis of Electroencephalographic Data"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"

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