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 "sample and accumulate algorithms for belief updating in bayes networks"):
  Abstract: Belief updating in Bayes nets, a well known computationally hard problem, has recently been approximated by several deterministic algorithms, and by various randomized approximation algorithms. Deterministic algorithms usually provide probability bounds, but have an exponential runtime. Some randomized schemes have a polynomial runtime, but provide only probability estimates. We present randomized algorithms that enumerate high-probability partial instantiations, resulting in probability bounds. Some of these algorithms are also sampling algorithms. Specifically, we introduce and evaluate a variant of backward sampling, both as a sampling algorithm and as a randomized enumeration algorithm. We also relax the implicit assumption used by both sampling and accumulation algorithms, that query nodes must be instantiated in all the samples.

Includes members:
  Member "some properties of joint probability distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "flexible policy construction by information refinement")
  Member "the use of conflicts in searching bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "a stratified simulation scheme for inference in bayesian belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monte carlo inference via greedy importance sampling")
  Member "backward simulation in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using deep neural network approximate bayesian network")
  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 "state space abstraction for anytime evaluation of probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "incremental probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using qualitative relationships for bounding probability distributions")

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)