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: "Free Energy Minimization Algorithm for Decoding and Cryptanalysis three binary vectors: s of length N"
  Abstract: where A is a binary matrix. Our task is to infer s given z and A, and given assumptions about the statistical properties of s and n. This problem arises in the decoding of a noisy signal transmitted using a linear code A, and in the inference of the sequence of a linear feedback shift register (LFSR) from noisy observations [1, 2].

Connected through hyperedge HE-1 (induced by source paper "Ensemble Learning and Evidence Maximization"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-2 (induced by source paper "Developments in Probabilistic Modelling with Neural Networks|Ensemble Learning"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "Ensemble Learning and Evidence Maximization"
    - "Ensemble Learning for Hidden Markov Models"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"

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