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: "The Automated Mapping of Plans for Plan Recognition"
  Abstract: To coordinate with other agents in its environment, an agent needs models of what the other agents are trying to do. When communication is impossible or expensive, this information must be acquired indirectly via plan recognition. Typical approaches to plan recognition start with a specification of the possible plans the other agents may be following, and develop special techniques for discriminating among the possibilities. Perhaps more desirable would be a uniform procedure for mapping plans to general structures supporting inference based on uncertain and incomplete observations. In this paper, we describe a set of methods for converting plans represented in a flexible procedural language to observation models represented as probabilistic belief networks.

Connected through hyperedge HE-1 (induced by source paper "Accounting for Context in Plan Recognition, with Application to Traffic Monitoring"):
  with members:
    - "The BATmobile: Towards a Bayesian Automated Taxi"

Connected through hyperedge HE-2 (induced by source paper "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"):
  with members:
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Abstract"
    - "Bayesian Graphical Modeling for Intelligent Tutoring Systems"

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