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: "GA-RBF: A Self-Optimising RBF Network"
  Abstract: The effects of a neural network's topology on its performance are well known, yet the question of finding optimal configurations automatically remains largely open. This paper proposes a solution to this problem for RBF networks. A self- optimising approach, driven by an evolutionary strategy, is taken. The algorithm uses output information and a computationally efficient approximation of RBF networks to optimise the K-means clustering process by co-evolving the two determinant parameters of the network's layout: the number of centroids and the centroids' positions. Empirical results demonstrate promise.

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"
    - "FONN: Combining First Order Logic with Connectionist Learning"
    - "GROWING RADIAL BASIS FUNCTION NETWORKS"
    - "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 RADIAL BASIS FUNCTION NETWORKS"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Learning networks for face analysis and synthesis"
    - "Learning Controllers for Industrial Robots"
    - "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"

Connected through hyperedge HE-4 (induced by source paper "Evolving Fuzzy Prototypes for Efficient Data Clustering"):
  with members:
    - "SUPERVISED COMPETITIVE LEARNING FOR FINDING POSITIONS OF RADIAL BASIS FUNCTIONS"

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