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: "Coupled hidden Markov models for complex action recognition"
  Abstract: c flMIT Media Lab Perceptual Computing / Learning and Common Sense Technical Report 407 20nov96 Abstract We present algorithms for coupling and training hidden Markov models (HMMs) to model interacting processes, and demonstrate their superiority to conventional HMMs in a vision task classifying two-handed actions. HMMs are perhaps the most successful framework in perceptual computing for modeling and classifying dynamic behaviors, popular because they offer dynamic time warping, a training algorithm, and a clear Bayesian semantics. However, the Markovian framework makes strong restrictive assumptions about the system generating the signalthat it is a single process having a small number of states and an extremely limited state memory. The single-process model is often inappropriate for vision (and speech) applications, resulting in low ceilings on model performance. Coupled HMMs provide an efficient way to resolve many of these problems, and offer superior training speeds, model likelihoods, and robustness to initial conditions.

Connected through hyperedge HE-1 (induced by source paper "Hidden Markov decision trees"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Factorial Hidden Markov Models"

Connected through hyperedge HE-2 (induced by source paper "Factorial Hidden Markov Models"):
  with members:
    - "Structured Representation of Complex Stochastic Systems"
    - "Hidden Markov decision trees"
    - "IMPROVING THE MEAN FIELD APPROXIMATION VIA THE USE OF MIXTURE DISTRIBUTIONS"
    - "Tractable Inference for Complex Stochastic Processes"
    - "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Speech Recognition with Dynamic Bayesian Networks"

Connected through hyperedge HE-3 (induced by source paper "Boltzmann Chains and Hidden Markov Models"):
  with members:
    - "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Learning in Boltzmann Trees"
    - "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"

Connected through hyperedge HE-4 (induced by source paper "Coupled hidden Markov models for modeling interacting processes"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Probabilistic Independence Networks for Hidden Markov Probability Models"):
  with members:
    - "On Structured Variational Approximations"
    - "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View"
    - "Tractable Inference for Complex Stochastic Processes"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Belief Propagation and Revision in Networks with Loops"
    - "Speech Recognition with Dynamic Bayesian Networks"

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