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: "Modeling Belief in Dynamic Systems. Part I: Foundations"
  Abstract: Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations of belief change. We claim that a better understanding of belief change can be gained from examining appropriate semantic models. In this paper we propose a general framework in which to model belief change. We begin by defining belief in terms of knowledge and plausibility: an agent believes if he knows that is more plausible than :. We then consider some properties defining the interaction between knowledge and plausibility, and show how these properties affect the properties of belief. In particular, we show that by assuming two of the most natural properties, belief becomes a KD45 operator. Finally, we add time to the picture. This gives us a framework in which we can talk about knowledge, plausibility (and hence belief), and time, which extends the framework of Halpern and Fagin for modeling knowledge in multi-agent systems. We then examine the problem of "minimal change". This notion can be captured by using prior plausibilities, an analogue to prior probabilities, which can be updated by "conditioning". We show by example that conditioning on a plausibility measure can capture many scenarios of interest. In a companion paper, we show how the two best-studied scenarios of belief change, belief revision and belief update, fit into our framework. ? Some of this work was done while both authors were at the IBM Almaden Research Center. The first author was also at Stanford while much of the work was done. IBM and Stanford's support are gratefully acknowledged. The work was also supported in part by the Air Force Office of Scientific Research (AFSC), under Contract F49620-91-C-0080 and grant F94620-96-1-0323 and by NSF under grants IRI-95-03109 and IRI-96-25901. A preliminary version of this paper appears in Proceedings of the 5th Conference on Theoretical Aspects of Reasoning About Knowledge, 1994, pp. 44-64, under the title "A knowledge-based framework for belief change, Part I: Foundations".

Connected through hyperedge HE-1 (induced by source paper "A Qualitative Markov Assumption and Its Implications for Belief Change"):
  with members:
    - "Generalized Update: Belief Change in Dynamic Settings"
    - "A Knowledge-Based Framework for Belief Change Part I: Foundations"
    - "Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions."
    - "Plausibility Measures: A User's Guide"
    - "Plausibility Measures and Default Reasoning"

Connected through hyperedge HE-2 (induced by source paper "A LOGICAL APPROACH TO REASONING ABOUT UNCERTAINTY: A TUTORIAL"):
  with members:
    - "A Knowledge-Based Framework for Belief Change Part I: Foundations"

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