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: "Interpretable Neural Networks with BP-SOM"
  Abstract: Interpretation of models induced by artificial neural networks is often a difficult task. In this paper we focus on a relatively novel neural network architecture and learning algorithm, bp-som, that offers possibilities to overcome this difficulty. It is shown that networks trained with bp-som show interesting regularities, in that hidden-unit activations become restricted to discrete values, and that the som part can be exploited for automatic rule extraction.

Connected through hyperedge HE-1 (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-2 (induced by source paper "Measuring the Difficulty of Specific Learning Problems"):
  with members:
    - "Simple Genetic Programming for Supervised Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Avoiding Overfitting with BP-SOM"
    - "Is Transfer Inductive?"

Connected through hyperedge HE-3 (induced by source paper "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Avoiding Overfitting with BP-SOM"
    - "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"
    - "Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping"
    - "A BENCHMARK FOR CLASSIFIER LEARNING"
    - "Adaptive Parameter Pruning in Neural Networks"
    - "An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "Avoiding Overfitting with BP-SOM"):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"

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