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 BATmobile: Towards a Bayesian Automated Taxi"
  Abstract: The problem of driving an autonomous vehicle in highway traffic engages many areas of AI research and has substantial economic significance. We describe work in progress on a new approach to this problem based on a decision-theoretic architecture using dynamic probabilistic networks. The architecture provides a sound solution to the problems of sensor noise, sensor failure, and uncertainty about the behavior of other vehicles and about the effects of one's own actions. Our approach has been implemented in a computer simulation system, and the autonomous vehicle successfully negotiates a variety of difficult situations.

Connected through hyperedge HE-1 (induced by source paper "Rationality and Intelligence"):
  with members:
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "Toward Efficient Agnostic Learning"
    - "A Formalization of Explanation-Based Macro-operator Learning"

Connected through hyperedge HE-2 (induced by source paper "Accounting for Context in Plan Recognition, with Application to Traffic Monitoring"):
  with members:
    - "The Automated Mapping of Plans for Plan Recognition"

Connected through hyperedge HE-3 (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"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-4 (induced by source paper "Fall Diagnosis using Dynamic Belief Networks"):
  with members:
    - "Dynamic Belief Networks for Discrete Monitoring"

Connected through hyperedge HE-5 (induced by source paper "Space-efficient inference in dynamic probabilistic networks"):
  with members:
    - "Factorial Hidden Markov Models"
    - "Probabilistic Independence Networks for Hidden Markov Probability Models"

Connected through hyperedge HE-6 (induced by source paper "Adaptive probabilistic networks"):
  with members:
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"

Connected through hyperedge HE-7 (induced by source paper "Stochastic simulation algorithms for dynamic probabilistic networks"):
  with members:
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "Structured Representation of Complex Stochastic Systems"
    - "Dynamic Belief Networks for Discrete Monitoring"
    - "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