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: "Quicknet on MultiSpert: Fast Parallel Neural Network Training"
  Abstract: The MultiSpert parallel system is a straight-forward extension of the Spert workstation accelerator, which is predominantly used in speech recognition research at ICSI. In order to deliver high performance for Artificial Neural Network training without requiring changes to the user interfaces, the exisiting Quicknet ANN library was modified to run on MultiSpert. In this report, we present the algorithms used in the parallelization of the Quicknet code and analyse their communication and computation requirements. The resulting performance model yields a better understanding of system speed-ups and potential bottlenecks. Experimental results from actual training runs validate the model and demonstrate the achieved performance levels.

Connected through hyperedge HE-1 (induced by source paper "MBP on T0: mixing floating- and fixed-point formats in BP learning"):
  with members:
    - "In  Fast Non-Linear Dimension Reduction"

Connected through hyperedge HE-2 (induced by source paper "SPERT-II: A Vector Microprocessor System and its Application to Large Problems in Backpropagation Training"):
  with members:
    - "A Fast Kohonen Net Implementation for"

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