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: "GAL: Networks that grow when they learn and shrink when they forget"
  Abstract: Learning when limited to modification of some parameters has a limited scope; the capability to modify the system structure is also needed to get a wider range of the learnable. In the case of artificial neural networks, learning by iterative adjustment of synaptic weights can only succeed if the network designer predefines an appropriate network structure, i.e., number of hidden layers, units, and the size and shape of their receptive and projective fields. This paper advocates the view that the network structure should not, as usually done, be determined by trial-and-error but should be computed by the learning algorithm. Incremental learning algorithms can modify the network structure by addition and/or removal of units and/or links. A survey of current connectionist literature is given on this line of thought. "Grow and Learn" (GAL) is a new algorithm that learns an association at one-shot due to being incremental and using a local representation. During the so-called "sleep" phase, units that were previously stored but which are no longer necessary due to recent modifications are removed to minimize network complexity. The incrementally constructed network can later be finetuned off-line to improve performance. Another method proposed that greatly increases recognition accuracy is to train a number of networks and vote over their responses. The algorithm and its variants are tested on recognition of handwritten numerals and seem promising especially in terms of learning speed. This makes the algorithm attractive for on-line learning tasks, e.g., in robotics. The biological plausibility of incremental learning is also discussed briefly. Earlier part of this work was realized at the Laboratoire de Microinformatique of Ecole Polytechnique Federale de Lausanne and was supported by the Fonds National Suisse de la Recherche Scientifique. Later part was realized at and supported by the International Computer Science Institute. A number of people helped by guiding, stimulating discussions or questions: Subutai Ahmad, Peter Clarke, Jerry Feldman, Christian Jutten, Pierre Marchal, Jean Daniel Nicoud, Steve Omohondro and Leon Personnaz.

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"
    - "Designing Neural Networks for Adaptive Control"
    - "GA-RBF: A Self-Optimising RBF Network"
    - "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 "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."):
  with members:
    - "Multiassociative Memory"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Neural Network Applicability: Classifying the Problem Space"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"

Connected through hyperedge HE-3 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"

Connected through hyperedge HE-4 (induced by source paper "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "An Improved Algorithm for Incremental Induction of Decision Trees"
    - "Inductive Learning and Case-Based Reasoning"
    - "Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics"

Connected through hyperedge HE-5 (induced by source paper "Comparison of Kernel Estimators, Perceptrons, and Radial-Basis Functions for OCR and Speech Classification"):
  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"

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