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 "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"):
  Abstract: Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm.#R##N##R##N#We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PATHFINDER network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.

Includes members:
  Member "search based methods to bound diagnostic probabilities in very large belief nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time top k predictive query processing over event streams")
  Member "computational investigation of low discrepancy sequences in simulation algorithms for bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refractor importance sampling")
  Member "adaptive importance sampling for estimation in structured domains":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "correlated equilibria for approximate variational inference in mrfs")
  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 "a generalization of the noisy or model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks")
  Member "parameter adjustment in bayes networks the generalized noisy or gate":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepfense online accelerated defense against adversarial deep learning")
  Member "weighing and integrating evidence for stochastic simulation in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gaussian mixture reduction for time constrained approximate inference in hybrid bayesian networks")

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)