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 "weighted regret based likelihood a new approach to describing uncertainty"):
  Abstract: Recently, Halpern and Leung suggested representing uncertainty by a weighted set of probability measures, and suggested a way of making decisions based on this representation of uncertainty: maximizing weighted regret. Their paper does not answer an apparently simpler question: what it means, according to this representation of uncertainty, for an event E to be more likely than an event E'. In this paper, a notion of comparative likelihood when uncertainty is represented by a weighted set of probability measures is defined. It generalizes the ordering defined by probability (and by lower probability) in a natural way; a generalization of upper probability can also be defined. A complete axiomatic characterization of this notion of regret-based likelihood is given.

Includes members:
  Member "defining relative likelihood in partially ordered preferential structures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified framework for order of magnitude confidence relations")
  Member "a logic for reasoning about upper probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the craft of model making pspace bounds for non iterative modal logics")
  Member "independence concepts for convex sets of probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "separation properties of sets of probability measures")
  Member "do we need higher order probabilities and if so what do they mean":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the random conditional distribution for higher order probabilistic inference")
  Member "calculating uncertainty intervals from conditional convex sets of probabilities":
    also connected through other hyperedges:
      (none)
  Member "weighted sets of probabilities and minimaxweighted expected regret new approaches for representing uncertainty and making decisions":
    also connected through other hyperedges:
      (none)

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)