human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a new probabilistic transformation of belief mass assignment"
  Abstract: In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the DSmP transformation works and we compare it to main existing transformations proposed in the literature so far. We show the advantages of DSmP over classical transformations in term of Probabilistic Information Content (PIC). The direct extension of this transformation for dealing with qualitative belief assignments is also presented.

Connected through hyperedge HE-1 (induced by source paper "d numbers theory a generalization of dempster shafer theory"):
  with members:
    - "an introduction to the dsm theory for the combination of paradoxical uncertain and imprecise sources of information"
    - "updating beliefs with incomplete observations"
    - "a modal logic framework for multi agent belief fusion"
    - "a new technique for combining multiple classifiers using the dempster shafer theory of evidence"

Connected through hyperedge HE-2 (induced by source paper "an introduction to dsmt"):
  with members:
    - "pignistic probability transforms for mixes of low and high probability events"
    - "uniform and partially uniform redistribution rules"
    - "target type tracking with pcr5 and dempster s rules a comparative analysis"
    - "dsmt a new paradigm shift for information fusion"

Connected through hyperedge HE-3 (induced by source paper "implementing general belief function framework with a practical codification for low complexity"):
  with members:
    - "practical uses of belief functions"
    - "generalized proportional conflict redistribution rule applied to sonar imagery and radar targets classification"
    - "human expert fusion for image classification"
    - "a new generalization of the proportional conflict redistribution rule stable in terms of decision"
    - "decision support with belief functions theory for seabed characterization"
    - "toward a combination rule to deal with partial conflict and specificity in belief functions theory"
    - "proportional conflict redistribution rules for information fusion"
    - "the generalized pignistic transformation"

Question: Please tell me which class the center node 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)