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: "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
  Abstract: 2] D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Internal Representations by Error Propagation", in D. E. Rumelhart and J. L. McClelland (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1), MIT Press (1986).

Connected through hyperedge HE-1 (induced by source paper "Soft Computing: the Convergence of Emerging Reasoning Technologies"):
  with members:
    - "Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques"
    - "Evolution of the Topology and the Weights of Neural Networks using Genetic Programming with a"
    - "Genetic Algorithms for Automated Tuning of Fuzzy Controllers: A Transportation Application"

Connected through hyperedge HE-2 (induced by source paper "SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "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-3 (induced by source paper "Temporal Compositional Processing by a DSOM Hierarchical Model"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Learning networks for face analysis and synthesis"

Connected through hyperedge HE-4 (induced by source paper "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"):
  with members:
    - "Introduction to Radial Basis Function Networks"
    - "GROWING RADIAL BASIS FUNCTION NETWORKS"
    - "Scatter-partitioning RBF network for function regression and image"
    - "Evolving Artificial Neural Networks using the Baldwin Effect"
    - "LBG-U method for vector quantization an improvement over LBG inspired from neural networks"
    - "Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map"

Connected through hyperedge HE-5 (induced by source paper "Using Many-Particle Decomposition to get a Parallel Self-Organising Map"):
  with members:
    - "Approximation with neural networks: Between local and global approximation"

Connected through hyperedge HE-6 (induced by source paper "Incremental Polynomial Model-Controller Network: a self organising non-linear controller"):
  with members:
    - "Learning networks for face analysis and synthesis"

Connected through hyperedge HE-7 (induced by source paper "Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Using Sampling and Queries to Extract Rules from Trained Neural Networks"
    - "Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA"
    - "Rearrangement of receptive field topography after intracortical and peripheral stimulation: The role of plasticity in"

Connected through hyperedge HE-8 (induced by source paper "Forward models: Supervised learning with a distal teacher"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "A Cognitive Model of Learning to Navigate"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "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"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
    - "A Comparison of Action Selection Learning Methods"
    - "Computational Models of Sensorimotor Integration  Computational Maps and Motor Control."

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