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: "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"
  Abstract: 190 articles about neural network learning algorithms published in 1993 and 1994 are examined for the amount of experimental evaluation they contain. 29% of them employ not even a single realistic or real learning problem. Only 8% of the articles present results for more than one problem using real world data. Furthermore, one third of all articles do not present any quantitative comparison with a previously known algorithm. These results suggest that we should strive for better assessment practices in neural network learning algorithm research. For the long-term benefit of the field, the publication standards should be raised in this respect and easily accessible collections of benchmark problems should be built.

Connected through hyperedge HE-1 (induced by source paper "Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"

Connected through hyperedge HE-2 (induced by source paper "Connection Pruning with Static and Adaptive Pruning Schedules"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "CuPit-2: Portable and Efficient High-Level Parallel Programming of Neural Networks for the Systems Analysis Modelling"

Connected through hyperedge HE-3 (induced by source paper "Adaptive Parameter Pruning in Neural Networks"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "A Parallel Programming Model for Irregular Dynamic Neural Networks a programming model that allows to"

Connected through hyperedge HE-4 (induced by source paper "Averaging and Data Snooping"):
  with members:
    - "Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder"
    - "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"

Connected through hyperedge HE-5 (induced by source paper "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"):
  with members:
    - "On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance"
    - "Averaging and Data Snooping"

Connected through hyperedge HE-6 (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"
    - "Interpretable Neural Networks with BP-SOM"
    - "Avoiding Overfitting with BP-SOM"
    - "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-7 (induced by source paper "Comparison of Bayesian and Neural Net Unsupervised Classification Techniques"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."

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