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: "on the relation between kappa calculus and probabilistic reasoning"
  Abstract: We study the connection between kappa calculus and probabilistic reasoning in diagnosis applications. Specifically, we abstract a probabilistic belief network for diagnosing faults into a kappa network and compare the ordering of faults computed using both methods. We show that, at least for the example examined, the ordering of faults coincide as long as all the causal relations in the original probabilistic network are taken into account. We also provide a formal analysis of some network structures where the two methods will differ. Both kappa rankings and infinitesimal probabilities have been used extensively to study default reasoning and belief revision. But little has been done on utilizing their connection as outlined above. This is partly because the relation between kappa and probability calculi assumes that probabilities are arbitrarily close to one (or zero). The experiments in this paper investigate this relation when this assumption is not satisfied. The reported results have important implications on the use of kappa rankings to enhance the knowledge engineering of uncertainty models.

Connected through hyperedge HE-1 (induced by source paper "factored probabilistic belief tracking"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "tractable inference for complex stochastic processes"
    - "context specific independence in bayesian networks"
    - "a general non probabilistic theory of inductive reasoning"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "a simple insight into iterative belief propagation s success"

Connected through hyperedge HE-2 (induced by source paper "query dags a practical paradigm for implementing belief network inference"):
  with members:
    - "global conditioning for probabilistic inference in belief networks"

Connected through hyperedge HE-3 (induced by source paper "a qualitative linear utility theory for spohn s theory of epistemic beliefs"):
  with members:
    - "arguing for decisions a qualitative model of decision making"
    - "an order of magnitude calculus"
    - "a general non probabilistic theory of inductive reasoning"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-4 (induced by source paper "belief updating and learning in semi qualitative probabilistic networks"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "elicitation of probabilities for belief networks combining qualitative and quantitative information"
    - "exploiting qualitative knowledge in the learning of conditional probabilities of bayesian networks"
    - "upgrading ambiguous signs in qpns"
    - "monotonicity in bayesian networks"
    - "complexity results and approximation strategies for map explanations"

Connected through hyperedge HE-5 (induced by source paper "order of magnitude influence diagrams"):
  with members:
    - "an order of magnitude calculus"
    - "from influence diagrams to junction trees"
    - "qualitative mdps and pomdps an order of magnitude approximation"

Connected through hyperedge HE-6 (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"
    - "irrelevance and independence relations in quasi bayesian networks"
    - "conditional independence in possibility theory"
    - "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)