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: "plausibility measures a user s guide"
  Abstract: We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered set. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. The lack of structure in a plausibility measure makes it easy for us to add structure on an "as needed" basis, letting us examine what is required to ensure that a plausibility measure has certain properties of interest. This gives us insight into the essential features of the properties in question, while allowing us to prove general results that apply to many approaches to reasoning about uncertainty. Plausibility measures have already proved useful in analyzing default reasoning. In this paper, we examine their "algebraic properties," analogues to the use of + and * in probability theory. An understanding of such properties will be essential if plausibility measures are to be used in practice as a representation tool.

Connected through hyperedge HE-1 (induced by source paper "compact representations of extended causal models"):
  with members:
    - "axiomatizing causal reasoning"
    - "graded causation and defaults"
    - "actual causation and the art of modeling"
    - "defaults and normality in causal structures"
    - "nonmonotonic reasoning preferential models and cumulative logics"
    - "conditional plausibility measures and bayesian networks"
    - "plausibility measures and default reasoning"

Connected through hyperedge HE-2 (induced by source paper "axiomatic foundations for a class of generalized expected utility algebraic expected utility"):
  with members:
    - "an order of magnitude calculus"
    - "a qualitative linear utility theory for spohn s theory of epistemic beliefs"
    - "a general non probabilistic theory of inductive reasoning"
    - "general belief measures"
    - "a comparison of axiomatic approaches to qualitative decision making using possibility theory"
    - "decision making for symbolic probability"
    - "qualitative decision making under possibilistic uncertainty toward more discriminating criteria"
    - "decision making with partially consonant belief functions"

Connected through hyperedge HE-3 (induced by source paper "an algebraic graphical model for decision with uncertainties feasibilities and utilities"):
  with members:
    - "a possibilistic model for qualitative sequential decision problems under uncertainty in partially observable environments"
    - "qualitative models for decision under uncertainty without the commensurability assumption"
    - "an order of magnitude calculus"
    - "graphical models for preference and utility"
    - "a qualitative linear utility theory for spohn s theory of epistemic beliefs"
    - "context specific independence in bayesian networks"
    - "a general non probabilistic theory of inductive reasoning"
    - "general belief measures"

Connected through hyperedge HE-4 (induced by source paper "great expectations part ii generalized expected utility as a universal decision rule"):
  with members:
    - "a comparison of axiomatic approaches to qualitative decision making using possibility theory"
    - "great expectations part i on the customizability of generalized expected utility"
    - "expected qualitative utility maximization"
    - "conditional plausibility measures and bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "great expectations part i on the customizability of generalized expected utility"):
  with members:
    - "generalized qualitative probability savage revisited"
    - "a comparison of axiomatic approaches to qualitative decision making using possibility theory"
    - "great expectations part ii generalized expected utility as a universal decision rule"
    - "conditional plausibility measures and bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "conditional plausibility measures and bayesian networks"):
  with members:
    - "independence concepts for convex sets of probabilities"
    - "separation properties of sets of probability measures"
    - "on the logic of causal models"
    - "general belief measures"
    - "irrelevance and independence relations in quasi bayesian networks"
    - "conditional independence in possibility theory"
    - "on the relation between kappa calculus and probabilistic reasoning"
    - "an ordinal view of independence with application to plausible reasoning"

Connected through hyperedge HE-7 (induced by source paper "plausibility measures and default reasoning"):
  with members:
    - "a qualitative markov assumption and its implications for belief change"
    - "general belief measures"
    - "from statistical knowledge bases to degrees of belief"
    - "what does a conditional knowledge base entail"
    - "nonmonotonic reasoning preferential models and cumulative logics"
    - "conditional plausibility measures and bayesian networks"

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)