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: "MULTIPLE SCALES OF BRAIN-MIND INTERACTIONS"
  Abstract: Posner and Raichle's Images of Mind is an excellent educational book and very well written. Some aws as a scientific publication are: (a) the accuracy of the linear subtraction method used in PET is subject to scrutiny by further research at finer spatial-temporal resolutions; (b) lack of accuracy of the experimental paradigm used for EEG complementary studies. Images (Posner & Raichle, 1994) is an excellent introduction to interdisciplinary research in cognitive and imaging science. Well written and illustrated, it presents concepts in a manner well suited both to the layman/undergraduate and to the technical nonexpert/graduate student and postdoctoral researcher. Many, not all, people involved in interdisciplinary neuroscience research agree with the P & R's statements on page 33, on the importance of recognizing emergent properties of brain function from assemblies of neurons. It is clear from the sparse references that this book was not intended as a standalone review of a broad field. There are some aws in the scientific development, but this must be expected in such a pioneering venture. P & R hav e proposed many cognitive mechanisms deserving further study with imaging tools yet to be developed which can yield better spatial-temporal resolutions.

Connected through hyperedge HE-1 (induced by source paper "Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG"):
  with members:
    - "STATISTICAL MECHANICS OF COMBAT WITH HUMAN FACTORS"
    - "Path-integral evolution of chaos embedded in noise: Duffing neocortical analog"
    - "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"

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