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: "irrelevance and independence relations in quasi bayesian networks"
  Abstract: This paper analyzes irrelevance and independence relations in graphical models associated with convex sets of probability distributions (called Quasi-Bayesian networks). The basic question in Quasi-Bayesian networks is, How can irrelevance/independence relations in Quasi-Bayesian networks be detected, enforced and exploited? This paper addresses these questions through Walley's definitions of irrelevance and independence. Novel algorithms and results are presented for inferences with the so-called natural extensions using fractional linear programming, and the properties of the so-called type-1 extensions are clarified through a new generalization of d-separation.

Connected through hyperedge HE-1 (induced by source paper "credal networks under maximum entropy"):
  with members:
    - "coherent knowledge processing at maximum entropy by spirit"
    - "generating bayesian networks from probability logic knowledge bases"
    - "decision making with interval influence diagrams"
    - "from statistical knowledge bases to degrees of belief"
    - "probabilistic default reasoning with conditional constraints"
    - "probabilistic deduction with conditional constraints over basic events"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-2 (induced by source paper "separation properties of sets of probability measures"):
  with members:
    - "independence with lower and upper probabilities"
    - "independence concepts for convex sets of probabilities"
    - "robustness analysis of bayesian networks with local convex sets of distributions"

Connected through hyperedge HE-3 (induced by source paper "credal networks under epistemic irrelevance"):
  with members:
    - "epistemic irrelevance in credal nets the case of imprecise markov trees"

Connected through hyperedge HE-4 (induced by source paper "conditional plausibility measures and bayesian networks"):
  with members:
    - "plausibility measures a user s guide"
    - "independence concepts for convex sets of probabilities"
    - "separation properties of sets of probability measures"
    - "on the logic of causal models"
    - "general belief measures"
    - "conditional independence in possibility theory"
    - "on the relation between kappa calculus and probabilistic reasoning"
    - "an ordinal view of independence with application to plausible reasoning"

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)