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 "measuring the hardness of stochastic sampling on bayesian networks with deterministic causalities the k test"):
  Abstract: Approximate Bayesian inference is NP-hard. Dagum and Luby defined the Local Variance Bound (LVB) to measure the approximation hardness of Bayesian inference on Bayesian networks, assuming the networks model strictly positive joint probability distributions, i.e. zero probabilities are not permitted. This paper introduces the k-test to measure the approximation hardness of inference on Bayesian networks with deterministic causalities in the probability distribution, i.e. when zero conditional probabilities are permitted. Approximation by stochastic sampling is a widely-used inference method that is known to suffer from inefficiencies due to sample rejection. The k-test predicts when rejection rates of stochastic sampling a Bayesian network will be low, modest, high, or when sampling is intractable.

Includes members:
  Member "knowledge engineering for large belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "simulation approaches to general probabilistic inference on belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constraint based inference in probabilistic logic programs")
  Member "an importance sampling algorithm based on evidence pre propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cutset sampling with likelihood weighting")
  Member "approximate inference algorithms for hybrid bayesian networks with discrete constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the approximability of weighted model integration on dnf structures")
  Member "mixtures of deterministic probabilistic networks and their and or search space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "world knowledge as indirect supervision for document clustering")
  Member "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "universal marginaliser for deep amortised inference for probabilistic programs")

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)