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 "join graph propagation algorithms"):
  Abstract: The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of iterative BP and IJGP by relating these algorithms to well known classes of constraint propagation schemes

Includes members:
  Member "a scheme for approximating probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tractable inference for complex stochastic processes")
  Member "expectation propagation for approximate bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")
  Member "value elimination bayesian inference via backtracking search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the sum product theorem a foundation for learning tractable models")
  Member "iterative join graph propagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate knowledge compilation by online collapsed importance sampling")
  Member "an importance sampling algorithm based on evidence pre propagation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cutset sampling with likelihood weighting")
  Member "a variational approach for approximating bayesian networks by edge deletion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "node splitting a scheme for generating upper bounds in bayesian networks")
  Member "structured region graphs morphing ep into gbp":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "region based approximation for high dimensional bayesian network models")
  Member "a simple insight into iterative belief propagation s success":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "factored probabilistic belief tracking")

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)