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 "exploiting contextual independence in probabilistic inference"):
  Abstract: Bayesian belief networks have grown to prominence because they provide compact representations for many problems for which probabilistic inference is appropriate, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probabilities of a variable given its parents. In this paper we present such a representation that exploits contextual independence in terms of parent contexts; which variables act as parents may depend on the value of other variables. The internal representation is in terms of contextual factors (confactors) that is simply a pair of a context and a table. The algorithm, contextual variable elimination, is based on the standard variable elimination algorithm that eliminates the nonquery variables in turn, but when eliminating a variable, the tables that need to be multiplied can depend on the context. This algorithm reduces to standard variable elimination when there is no contextual independence structure to exploit. We show how this can be much more efficient than variable elimination when there is structure to exploit. We explain why this new method can exploit more structure than previous methods for structured belief network inference and an analogous algorithm that uses trees.

Includes members:
  Member "context specific approximation in probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "representing and combining partially specified cpts")
  Member "exploiting the rule structure for decision making within the independent choice logic":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spudd stochastic planning using decision diagrams")
  Member "a bayesian approach to learning bayesian networks with local structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the latent variable interpretation in sum product networks")
  Member "learning bayesian networks with local structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a meta transfer objective for learning to disentangle causal mechanisms")
  Member "a new look at causal independence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "abstracting probabilistic models a logical perspective")
  Member "a sufficiently fast algorithm for finding close to optimal junction trees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a variational approximation for bayesian networks with discrete and continuous latent variables")
  Member "correlated action effects in decision theoretic regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spudd stochastic planning using decision diagrams")

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)