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: "Connection Pruning with Static and Adaptive Pruning Schedules"
  Abstract: Neural network pruning methods on the level of individual network parameters (e.g. connection weights) can improve generalization, as is shown in this empirical study. However, an open problem in the pruning methods known today (e.g. OBD, OBS, autoprune, epsiprune) is the selection of the number of parameters to be removed in each pruning step (pruning strength). This work presents a pruning method lprune that automatically adapts the pruning strength to the evolution of weights and loss of generalization during training. The method requires no algorithm parameter adjustment by the user. Results of statistical significance tests comparing autoprune, lprune, and static networks with early stopping are given, based on extensive experimentation with 14 different problems. The results indicate that training with pruning is often significantly better and rarely significantly worse than training with early stopping without pruning. Furthermore, lprune is often superior to autoprune (which is superior to OBD) on diagnosis tasks unless severe pruning early in the training process is required.

Connected through hyperedge HE-1 (induced by source paper "CuPit-2: Portable and Efficient High-Level Parallel Programming of Neural Networks for the Systems Analysis Modelling"):
  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"
    - "CuPit-2 A Parallel Language for Neural Algorithms: Language Reference and Tutorial"

Connected through hyperedge HE-2 (induced by source paper "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "Comparison of Bayesian and Neural Net Unsupervised Classification Techniques"
    - "Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping"
    - "Adaptive Parameter Pruning in Neural Networks"
    - "Averaging and Data Snooping"
    - "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"

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"
    - "Interpretable Neural Networks with BP-SOM"
    - "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"

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