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 A Parallel Language for Neural Algorithms: Language Reference and Tutorial"
  Abstract: and load balancing even for irregular neural networks. The idea to achieve these goals lies in the programming model: CuPit-2 programs are object-centered, with connections and nodes of a graph (which is the neural network) being the objects. Algorithms are based on parallel local computations in the nodes and connections and communication along the connections (plus broadcast and reduction operations). This report describes the design considerations and the resulting language definition and discusses in detail a tutorial example program. This CuPit-2 language manual and tutorial is an updated version of the original CuPit language manual [Pre94]. The new language CuPit-2 differs from the original CuPit in several ways. All language changes from CuPit to CuPit-2 are listed in the appendix.

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"
    - "Connection Pruning with Static and Adaptive Pruning Schedules"

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: Portable and Efficient High-Level Parallel Programming of Neural Networks for the Systems Analysis Modelling"

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