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: "Boltzmann Chains and Hidden Markov Models"
  Abstract: We propose a statistical mechanical framework for the modeling of discrete time series. Maximum likelihood estimation is done via Boltzmann learning in one-dimensional networks with tied weights. We call these networks Boltzmann chains and show that they contain hidden Markov models (HMMs) as a special case. Our framework also motivates new architectures that address particular shortcomings of HMMs. We look at two such architectures: parallel chains that model feature sets with disparate time scales, and looped networks that model long-term dependencies between hidden states. For these networks, we show how to implement the Boltzmann learning rule exactly, in polynomial time, without resort to simulated or mean-field annealing. The necessary computations are done by exact decimation procedures from statistical mechanics.

Connected through hyperedge HE-1 (induced by source paper "Learning in Boltzmann Trees"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Unsupervised learning of distributions on binary vectors using two layer networks"
    - "Decimatable Boltzmann Machines vs. Gibbs Sampling"

Connected through hyperedge HE-2 (induced by source paper "Exploiting Tractable Substructures in Intractable Networks"):
  with members:
    - "Hidden Markov decision trees"
    - "On Structured Variational Approximations"
    - "Computing upper and lower bounds on likelihoods in intractable networks"
    - "Recursive algorithms for approximating probabilities in graphical models"
    - "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
    - "Learning in Boltzmann Trees"
    - "Implicit learning in 3D object recognition: The importance of temporal context"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"

Connected through hyperedge HE-3 (induced by source paper "Coupled hidden Markov models for complex action recognition"):
  with members:
    - "Hidden Markov decision trees"
    - "Factorial Hidden Markov Models"
    - "Coupled hidden Markov models for modeling interacting processes"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-4 (induced by source paper "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"):
  with members:
    - "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"
    - "TD Models: Modeling the World at a Mixture of Time Scales"
    - "Diffusion of Context and Credit Information in Markovian Models"

Connected through hyperedge HE-5 (induced by source paper "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"):
  with members:
    - "GIBBS-MARKOV MODELS"
    - "GIBBS-MARKOV MODELS"
    - "GIBBS-MARKOV MODELS"
    - "Learning networks for face analysis and synthesis"

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