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: "Factorial Hidden Markov Models"
  Abstract: One of the basic probabilistic tools used for time series modeling is the hidden Markov model (HMM). In an HMM, information about the past of the time series is conveyed through a single discrete variable|the hidden state. We present a generalization of HMMs in which this state is factored into multiple state variables and is therefore represented in a distributed manner. Both inference and learning in this model depend critically on computing the posterior probabilities of the hidden state variables given the observations. We present an exact algorithm for inference in this model, and relate it to the Forward-Backward algorithm for HMMs and to algorithms for more general belief networks. Due to the combinatorial nature of the hidden state representation, this exact algorithm is intractable. As in other intractable systems, approximate inference can be carried out using Gibbs sampling or mean field theory. We also present a structured approximation in which the the state variables are decoupled, based on which we derive a tractable learning algorithm. Empirical comparisons suggest that these approximations are efficient and accurate alternatives to the exact methods. Finally, we use the structured approximation to model Bach's chorales and show that it outperforms HMMs in capturing the complex temporal patterns in this dataset.

Connected through hyperedge HE-1 (induced by source paper "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS"):
  with members:
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"

Connected through hyperedge HE-2 (induced by source paper "Tractable Inference for Complex Stochastic Processes"):
  with members:
    - "Structured Representation of Complex Stochastic Systems"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "The BATmobile: Towards a Bayesian Automated Taxi"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-3 (induced by source paper "Space-efficient inference in dynamic probabilistic networks"):
  with members:
    - "The BATmobile: Towards a Bayesian Automated Taxi"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-4 (induced by source paper "Speech Recognition with Dynamic Bayesian Networks"):
  with members:
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-5 (induced by source paper "Hidden Markov decision trees"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Coupled hidden Markov models for complex action recognition"

Connected through hyperedge HE-6 (induced by source paper "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"):
  with members:
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Exploiting Tractable Substructures in Intractable Networks"

Connected through hyperedge HE-7 (induced by source paper "Coupled hidden Markov models for complex action recognition"):
  with members:
    - "Hidden Markov decision trees"
    - "Coupled hidden Markov models for modeling interacting processes"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"
    - "Boltzmann Chains and Hidden Markov Models"

Connected through hyperedge HE-8 (induced by source paper "Structured Representation of Complex Stochastic Systems"):
  with members:
    - "Context-Specific Independence in Bayesian Networks"
    - "Global Conditioning for Probabilistic Inference in Belief Networks"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "Tractable Inference for Complex Stochastic Processes"

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