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: "Probabilistic Independence Networks for Hidden Markov Probability Models"
  Abstract: Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.

Connected through hyperedge HE-1 (induced by source paper "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"):
  with members:
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide"

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"
    - "Factorial Hidden Markov Models"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "The BATmobile: Towards a Bayesian Automated Taxi"

Connected through hyperedge HE-3 (induced by source paper "Belief Propagation and Revision in Networks with Loops"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Space-efficient inference in dynamic probabilistic networks"):
  with members:
    - "Factorial Hidden Markov Models"
    - "The BATmobile: Towards a Bayesian Automated Taxi"

Connected through hyperedge HE-5 (induced by source paper "Speech Recognition with Dynamic Bayesian Networks"):
  with members:
    - "Factorial Hidden Markov Models"

Connected through hyperedge HE-6 (induced by source paper "On Structured Variational Approximations"):
  with members:
    - "A VIEW OF THE EM ALGORITHM THAT JUSTIFIES INCREMENTAL, SPARSE, AND OTHER VARIANTS"
    - "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"
    - "Factorial Hidden Markov Models"
    - "Coupled hidden Markov models for modeling interacting processes"
    - "Boltzmann Chains and Hidden Markov Models"

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