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: "CuPit-2: Portable and Efficient High-Level Parallel Programming of Neural Networks for the Systems Analysis Modelling"
  Abstract: CuPit-2 is a special-purpose programming language designed for expressing dynamic neural network learning algorithms. It provides most of the flexibility of general-purpose languages such as C or C ++ , but is more expressive. It allows writing much clearer and more elegant programs, in particular for algorithms that change the network topology dynamically (constructive algorithms, pruning algorithms). In contrast to other languages, CuPit-2 programs can be compiled into efficient code for parallel machines without any changes in the source program, thus providing an easy start for using parallel platforms. This article analyzes the circumstances under which the CuPit-2 approach is the most useful one, presents a description of most language constructs and reports performance results for CuPit-2 on symmetric multiprocessors (SMPs). It concludes that in many cases CuPit-2 is a good basis for neural learning algorithm research on small-scale parallel machines.

Connected through hyperedge HE-1 (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"
    - "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"

Connected through hyperedge HE-2 (induced by source paper "A Parallel Programming Model for Irregular Dynamic Neural Networks a programming model that allows to"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "Adaptive Parameter Pruning in Neural Networks"
    - "CuPit-2 A Parallel Language for Neural Algorithms: Language Reference and Tutorial"

Connected through hyperedge HE-3 (induced by source paper "CuPit-2 A Parallel Language for Neural Algorithms: Language Reference and Tutorial"):
  with members:
    - "A Parallel Programming Model for Irregular Dynamic Neural Networks a programming model that allows to"

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