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 "Structured Representation of Complex Stochastic Systems"):
  Abstract: This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes. Unfortunately, they are often unwieldy since they cannot explicitly model the complex organizational structure of many real life systems: the fact that processes are typically composed of several interacting subprocesses, each of which can, in turn, be further decomposed. We propose a hierarchically structured representation language which extends both dynamic Bayesian networks and the object-oriented Bayesian network framework of [9], and show that our language allows us to describe such systems in a natural and modular way. Our language supports a natural representation for certain system characteristics that are hard to capture using more traditional frameworks. For example, it allows us to represent systems where some processes evolve at a different rate than others, or systems where the processes interact only intermittently. We provide a simple inference mechanism for our representation via translation to Bayesian networks, and suggest ways in which the inference algorithm can exploit the additional structure encoded in our representation.

Includes members:
  Member "Context-Specific Independence in Bayesian Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "The Frame Problem and Bayesian Network Action Representations")
  Member "Global Conditioning for Probabilistic Inference in Belief Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Automated Decomposition of Model-based Learning Problems")
  Member "Factorial Hidden Markov Models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "MIXED MEMORY MARKOV MODELS FOR TIME SERIES ANALYSIS")
  Member "Stochastic simulation algorithms for dynamic probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Tractable Inference for Complex Stochastic Processes")
  Member "Tractable Inference for Complex Stochastic Processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Factorial Hidden Markov Models")

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