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 "node splitting a scheme for generating upper bounds in bayesian networks"):
  Abstract: We formulate in this paper the mini-bucket algorithm for approximate inference in terms of exact inference on an approximate model produced by splitting nodes in a Bayesian network. The new formulation leads to a number of theoretical and practical implications. First, we show that branchand- bound search algorithms that use minibucket bounds may operate in a drastically reduced search space. Second, we show that the proposed formulation inspires new minibucket heuristics and allows us to analyze existing heuristics from a new perspective. Finally, we show that this new formulation allows mini-bucket approximations to benefit from recent advances in exact inference, allowing one to significantly increase the reach of these approximations.

Includes members:
  Member "empirical evaluation of approximation algorithms for probabilistic decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mini bucket heuristics for improved search")
  Member "systematic vs non systematic algorithms for solving the mpe task":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "map estimation linear programming and belief propagation with convex free energies")
  Member "iterative join graph propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate knowledge compilation by online collapsed importance sampling")
  Member "a variational approach for approximating bayesian networks by edge deletion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating the partition function by deleting and then correcting for model edges")
  Member "on the optimality of tree reweighted max product message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "partial optimality of dual decomposition for map inference in pairwise mrfs")
  Member "exploiting causal independence in bayesian network inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an advance on variable elimination with applications to tensor based computation")

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)