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: "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
  Abstract: Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response.

Connected through hyperedge HE-1 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Adaptive state space quantisation: adding and removing neurons"
    - "Adaptive state space quantisation for reinforcement learning of collision-free navigation"

Connected through hyperedge HE-2 (induced by source paper "Forecasting electricity demand using nonlinear mixture of experts"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Avoiding overfitting by locally matching the noise level of the data gating network discovers the"
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"

Connected through hyperedge HE-3 (induced by source paper "SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "How Lateral Interaction Develops in a Self-Organizing Feature Map"

Connected through hyperedge HE-4 (induced by source paper "ASOCS: A Multilayered Connectionist Network with Guaranteed Learning of Arbitrary Mappings"):
  with members:
    - "Models of Parallel Adaptive Logic"

Connected through hyperedge HE-5 (induced by source paper "Associative memory using action potential timing"):
  with members:
    - "An Efficient Implementation of Sigmoidal Neural Nets in Temporal Coding with Noisy Spiking Neurons"

Connected through hyperedge HE-6 (induced by source paper "Truth-from-Trash Learning and the Mobot"):
  with members:
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Parity: The Problem that Won't Go Away"

Connected through hyperedge HE-7 (induced by source paper "Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification"):
  with members:
    - "Learning networks for face analysis and synthesis"

Connected through hyperedge HE-8 (induced by source paper "EEG Signal Classification with Different Signal Representations  for a large number of hidden units."):
  with members:
    - "Determining Mental State from EEG Signals Using Parallel Implementations of Neural Networks"

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