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 "cutset sampling with likelihood weighting"):
  Abstract: The paper analyzes theoretically and empirically the performance of likelihood weighting (LW) on a subset of nodes in Bayesian networks. The proposed scheme requires fewer samples to converge due to reduction in sampling variance. The method exploits the structure of the network to bound the complexity of exact inference used to compute sampling distributions, similar to Gibbs cutset sampling. Yet, the extension of the previosly proposed cutset sampling principles to likelihood weighting is non-trivial due to differences in the sampling processes of Gibbs sampler and LW. We demonstrate empirically that likelihood weighting on a cutset (LWLC) is effective time-wise and has a lower rejection rate than LW when applied to networks with many deterministic probabilities. Finally, we show that the performance of likelihood weighting on a cutset can be improved further by caching computed sampling distributions and, consequently, learning 'zeros' of the target distribution.

Includes members:
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "backward simulation in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using deep neural network approximate bayesian network")
  Member "weighing and integrating evidence for stochastic simulation in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gaussian mixture reduction for time constrained approximate inference in hybrid bayesian networks")
  Member "knowledge engineering for large belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "empirical evaluation of approximation algorithms for probabilistic decoding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mini bucket heuristics for improved search")
  Member "simulation approaches to general probabilistic inference on belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constraint based inference in probabilistic logic programs")
  Member "rao blackwellised particle filtering for dynamic bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "poserbpf a rao blackwellized particle filter for 6d object pose tracking")
  Member "an importance sampling algorithm based on evidence pre propagation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "measuring the hardness of stochastic sampling on bayesian networks with deterministic causalities the k test")

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)