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 Parallel Programming Model for Irregular Dynamic Neural Networks a programming model that allows to"
  Abstract: A compiler for CuPit has been built for the MasPar MP-1/MP-2 using compilation techniques that can also be applied to most other parallel machines. The paper shortly presents the main ideas of the techniques used and results obtained by the various optimizations.

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"
    - "Connection Pruning with Static and Adaptive Pruning Schedules"
    - "CuPit-2 A Parallel Language for Neural Algorithms: Language Reference and Tutorial"

Connected through hyperedge HE-2 (induced by source paper "CuPit-2 A Parallel Language for Neural Algorithms: Language Reference and Tutorial"):
  with members:
    - "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 Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"

Connected through hyperedge HE-4 (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