human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cluster based specification techniques in dempster shafer theory"):
  Abstract: When reasoning with uncertainty there are many situations where evidences are not only uncertain but their propositions may also be weakly specified in the sense that it may not be certain to which event a proposition is referring. It is then crucial not to combine such evidences in the mistaken belief that they are referring to the same event. This situation would become manageable if the evidences could be clustered into subsets representing events that should be handled separately. In an earlier article we established within Dempster-Shafer theory a criterion function called the metaconflict function. With this criterion we can partition a set of evidences into subsets. Each subset representing a separate event. In this article we will not only find the most plausible subset for each piece of evidence, we will also find the plausibility for every subset that the evidence belongs to the subset. Also, when the number of subsets are uncertain we aim to find a posterior probability distribution regarding the number of subsets.

Includes members:
  Member "dempster s rule for evidence ordered in a complete directed acyclic graph":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding a posterior domain probability distribution by specifying nonspecific evidence")
  Member "on nonspecific evidence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "managing inconsistent intelligence")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.AI(Artificial Intelligence)