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: "Faster Learning in Multi-Layer Networks by Handling"
  Abstract: Generalized delta rule, popularly known as back-propagation (BP) [9, 5] is probably one of the most widely used procedures for training multi-layer feed-forward networks of sigmoid units. Despite reports of success on a number of interesting problems, BP can be excruciatingly slow in converging on a set of weights that meet the desired error criterion. Several modifications for improving the learning speed have been proposed in the literature [2, 4, 8, 1, 6]. BP is known to suffer from the phenomenon of flat spots [2]. The slowness of BP is a direct consequence of these flat-spots together with the formulation of the BP Learning rule. This paper proposes a new approach to minimizing the error that is suggested by the mathematical properties of the conventional error function and that effectively handles flat-spots occurring in the output layer. The robustness of the proposed technique is demonstrated on a number of data-sets widely studied in the machine learning community.

Connected through hyperedge HE-1 (induced by source paper "Generative Learning Structures and Processes for Generalized Connectionist Networks"):
  with members:
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "Properties of Genetic Representations of Neural Architectures"
    - "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"
    - "Pruning Strategies for the MTiling Constructive Learning Algorithm"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

Connected through hyperedge HE-2 (induced by source paper "Experiments with the Cascade-Correlation Algorithm"):
  with members:
    - "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"

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