human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning networks for face analysis and synthesis")
  Member "Exploiting Tractable Substructures in Intractable Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View")
  Member "Learning in Boltzmann Trees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Decimatable Boltzmann Machines vs. Gibbs Sampling")
  Member "Hierarchical Recurrent Neural Networks for Long-Term Dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "TD Models: Modeling the World at a Mixture of Time Scales")
  Member "Coupled hidden Markov models for complex action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Hidden Markov decision trees")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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