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 approach to updating beliefs"
  Abstract: We define a new notion of conditional belief, which plays the same role for Dempster-Shafer belief functions as conditional probability does for probability functions. Our definition is different from the standard definition given by Dempster, and avoids many of the well-known problems of that definition. Just as the conditional probability Pr (lB) is a probability function which is the result of conditioning on B being true, so too our conditional belief function Bel (lB) is a belief function which is the result of conditioning on B being true. We define the conditional belief as the lower envelope (that is, the inf) of a family of conditional probability functions, and provide a closed form expression for it. An alternate way of understanding our definition of conditional belief is provided by considering ideas from an earlier paper [Fagin and Halpern, 1989], where we connect belief functions with inner measures. In particular, we show here how to extend the definition of conditional probability to non measurable sets, in order to get notions of inner and outer conditional probabilities, which can be viewed as best approximations to the true conditional probability, given our lack of information. Our definition of conditional belief turns out to be an exact analogue of our definition of inner conditional probability.

Connected through hyperedge HE-1 (induced by source paper "an interval valued utility theory for decision making with dempster shafer belief functions"):
  with members:
    - "decision making with belief functions a review"

Connected through hyperedge HE-2 (induced by source paper "on the semantics and complexity of probabilistic logic programs"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "continuous time bayesian networks"
    - "probabilistic reasoning with answer sets"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-3 (induced by source paper "dempster shafer belief function a new interpretation"):
  with members:
    - "the recovery of causal poly trees from statistical data"

Connected through hyperedge HE-4 (induced by source paper "evidence updating for stream processing in big data robust conditioning in soft and hard fusion environments"):
  with members:
    - "practical uses of belief functions"

Connected through hyperedge HE-5 (induced by source paper "evidence against evidence theory"):
  with members:
    - "the recovery of causal poly trees from statistical data"

Connected through hyperedge HE-6 (induced by source paper "propagation of 2 monotone lower probabilities on an undirected graph"):
  with members:
    - "independence with lower and upper probabilities"
    - "from influence diagrams to junction trees"
    - "constraint propagation with imprecise conditional probabilities"
    - "minimal assumption distribution propagation in belief networks"
    - "optimal junction trees"
    - "quasi bayesian strategies for efficient plan generation application to the planning to observe problem"
    - "an efficient implementation of belief function propagation"
    - "towards precision of probabilistic bounds propagation"

Connected through hyperedge HE-7 (induced by source paper "a defect in dempster shafer theory"):
  with members:
    - "belief revision in probability theory"
    - "the transferable belief model and other interpretations of dempster shafer s model"
    - "the assumptions behind dempster s rule"

Connected through hyperedge HE-8 (induced by source paper "jeffrey s rule of conditioning generalized to belief functions"):
  with members:
    - "generalizing jeffrey conditionalization"
    - "the nature of the unnormalized beliefs encountered in the transferable belief model"

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)