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: "GROWING RADIAL BASIS FUNCTION NETWORKS"
  Abstract: This paper presents and evaluates two algorithms for incrementally constructing Radial Basis Function Networks, a class of neural networks which looks more suitable for adtaptive control applications than the more popular backpropagation networks. The first algorithm has been derived by a previous method developed by Fritzke, while the second one has been inspired by the CART algorithm developed by Breiman for generation regression trees. Both algorithms proved to work well on a number of tests and exhibit comparable performances. An evaluation on the standard case study of the Mackey-Glass temporal series is reported.

Connected through hyperedge HE-1 (induced by source paper "Learning Controllers for Industrial Robots"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "GAL: Networks that grow when they learn and shrink when they forget"
    - "Designing Neural Networks for Adaptive Control"
    - "GA-RBF: A Self-Optimising RBF Network"
    - "FONN: Combining First Order Logic with Connectionist Learning"
    - "Learning Concepts from Sensor Data of a Mobile Robot"

Connected through hyperedge HE-2 (induced by source paper "Learning networks for face analysis and synthesis"):
  with members:
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"
    - "Learning from incomplete data"
    - "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"
    - "Regularization Theory and Neural Networks Architectures"
    - "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression"
    - "Efficient Implementation of Gaussian Processes"

Connected through hyperedge HE-3 (induced by source paper "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Introduction to 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-4 (induced by source paper "GA-RBF: A Self-Optimising RBF Network"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Learning Controllers for Industrial Robots"
    - "Evolving Fuzzy Prototypes for Efficient Data Clustering"

Connected through hyperedge HE-5 (induced by source paper "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"):
  with members:
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"
    - "SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION"
    - "IEEE Learning the Semantic Similarity of Reusable Software Components"
    - "Self-Organization and Segmentation in a Laterally Connected Orientation Map of Spiking Neurons"

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