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: "Adaptive probabilistic networks"
  Abstract: Belief networks (or probabilistic networks) and neural networks are two forms of network representations that have been used in the development of intelligent systems in the field of artificial intelligence. Belief networks provide a concise representation of general probability distributions over a set of random variables, and facilitate exact calculation of the impact of evidence on propositions of interest. Neural networks, which represent parameterized algebraic combinations of nonlinear activation functions, have found widespread use as models of real neural systems and as function approximators because of their amenability to simple training algorithms. Furthermore, the simple, local nature of most neural network training algorithms provides a certain biological plausibility and allows for a massively parallel implementation. In this paper, we show that similar local learning algorithms can be derived for belief networks, and that these learning algorithms can operate using only information that is directly available from the normal, inferential processes of the networks. This removes the main obstacle preventing belief networks from competing with neural networks on the above-mentioned tasks. The precise, local, probabilistic interpretation of belief networks also allows them to be partially or wholly constructed by humans; allows the results of learning to be easily understood; and allows them to contribute to rational decision-making in a well-defined way.

Connected through hyperedge HE-1 (induced by source paper "Approximating Optimal Policies for Partially Observable Stochastic Domains"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Learning policies for partially observable environments: Scaling up"
    - "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"
    - "Efficient dynamic-programming updates in partially observable Markov decision processes"
    - "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"
    - "Rationality and Intelligence"

Connected through hyperedge HE-2 (induced by source paper "The BATmobile: Towards a Bayesian Automated Taxi"):
  with members:
    - "Rationality and Intelligence"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "Tractable Inference for Complex Stochastic Processes"
    - "Space-efficient inference in dynamic probabilistic networks"
    - "Accounting for Context in Plan Recognition, with Application to Traffic Monitoring"
    - "Fall Diagnosis using Dynamic Belief 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