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 "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"):
  Abstract: The hierarchical feature map system recognizes an input story as an instance of a particular script by classifying it at three levels: scripts, tracks and role bindings. The recognition taxonomy, i.e. the breakdown of each script into the tracks and roles, is extracted automatically and independently for each script from examples of script instantiations in an unsupervised self-organizing process. The process resembles human learning in that the differentiation of the most frequently encountered scripts become gradually the most detailed. The resulting structure is a hierachical pyramid of feature maps. The hierarchy visualizes the taxonomy and the maps lay out the topology of each level. The number of input lines and the self-organization time are considerably reduced compared to the ordinary single-level feature mapping. The system can recognize incomplete stories and recover the missing events. The taxonomy also serves as memory organization for script-based episodic memory. The maps assign a unique memory location for each script instantiation. The most salient parts of the input data are separated and most resources are concentrated on representing them accurately.

Includes members:
  Member "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex.":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Soft Computing: the Convergence of Emerging Reasoning Technologies")
  Member "SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "How Lateral Interaction Develops in a Self-Organizing Feature Map")
  Member "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION")

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