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: "Bits-back coding software guide"
  Abstract: Abstract | In this document, I first review the theory behind bits-back coding (aka. free energy coding) (Frey and Hinton 1996) and then describe the interface to C-language software that can be used for bits-back coding. This method is a new approach to the problem of optimal compression when a source code produces multiple codewords for a given symbol. It may seem that the most sensible codeword to use in this case is the shortest one. However, in the proposed bits-back approach, random codeword selection yields an effective codeword length that can be less than the shortest codeword length. If the random choices are Boltzmann distributed, the effective length is optimal for the given source code. The software which I describe in this guide is easy to use and the source code is only a few pages long. I illustrate the bits-back coding software on a simple quantized Gaussian mixture problem.

Connected through hyperedge HE-1 (induced by source paper "Free energy coding  In"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "A simple algorithm that discovers efficient perceptual codes"

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: Probabilistic_Methods