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: "MBP on T0: mixing floating- and fixed-point formats in BP learning"
  Abstract: We examine the efficient implementation of back prop type algorithms on T0 [4], a vector processor with a fixed point engine, designed for neural network simulation. A matrix formulation of back prop, Matrix Back Prop [1], has been shown to be very efficient on some RISCs [2]. Using Matrix Back Prop, we achieve an asymptotically optimal performance on T0 (about 0.8 GOPS) for both forward and backward phases, which is not possible with the standard on-line method. Since high efficiency is futile if convergence is poor (due to the use of fixed point arithmetic), we use a mixture of fixed and floating point operations. The key observation is that the precision of fixed point is sufficient for good convergence, if the range is appropriately chosen. Though the most expensive computations are implemented in fixed point, we achieve a rate of convergence that is comparable to the floating point version. The time taken for conversion between fixed and floating point is also shown to be reasonable.

Connected through hyperedge HE-1 (induced by source paper "In  Fast Non-Linear Dimension Reduction"):
  with members:
    - "Mixtures of Probabilistic Principal Component Analysers"
    - "Recognizing Handwritten Digits Using Mixtures of Linear Models"

Connected through hyperedge HE-2 (induced by source paper "Quicknet on MultiSpert: Fast Parallel Neural Network Training"):
  with members:
    - "SPERT-II: A Vector Microprocessor System and its Application to Large Problems in Backpropagation Training"

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