human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Multiassociative Memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Back Propagation is Sensitive to Initial Conditions")
  Member "Convergence-Zone Episodic Memory: Analysis and Simulations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2")
  Member "Topography And Ocular Dominance: A Model Exploring Positive Correlations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Analyzing Phase Transitions in High-Dimensional Self-Organizing Maps")
  Member "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Neural Network Model of Visual Tilt Aftereffects")
  Member "Neural Network Applicability: Classifying the Problem Space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based")
  Member "Competitive Anti-Hebbian Learning of Invariants":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "An information-maximisation approach to blind separation and blind deconvolution")
  Member "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Submitted to Circuits, Systems and Signal Processing Neural Network Constructive Algorithms: Trading Generalization for Learning Efficiency?")
  Member "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Machine Learning,  Reinforcement Learning with Replacing Eligibility Traces")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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