human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a variational approach for approximating bayesian networks by edge deletion"
  Abstract: We consider in this paper the formulation of approximate inference in Bayesian networks as a problem of exact inference on an approximate network that results from deleting edges (to reduce treewidth). We have shown in earlier work that deleting edges calls for introducing auxiliary network parameters to compensate for lost dependencies, and proposed intuitive conditions for determining these parameters. We have also shown that our method corresponds to IBP when enough edges are deleted to yield a polytree, and corresponds to some generalizations of IBP when fewer edges are deleted. In this paper, we propose a different criteria for determining auxiliary parameters based on optimizing the KL-divergence between the original and approximate networks. We discuss the relationship between the two methods for selecting parameters, shedding new light on IBP and its generalizations. We also discuss the application of our new method to approximating inference problems which are exponential in constrained treewidth, including MAP and nonmyopic value of information.

Connected through hyperedge HE-1 (induced by source paper "node splitting a scheme for generating upper bounds in bayesian networks"):
  with members:
    - "empirical evaluation of approximation algorithms for probabilistic decoding"
    - "systematic vs non systematic algorithms for solving the mpe task"
    - "iterative join graph propagation"
    - "on the optimality of tree reweighted max product message passing"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-2 (induced by source paper "approximating the partition function by deleting and then correcting for model edges"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "variational approximations between mean field theory and the junction tree algorithm"
    - "iterative join graph propagation"
    - "structured region graphs morphing ep into gbp"

Connected through hyperedge HE-3 (induced by source paper "inference by minimizing size divergence or their sum"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "improving the accuracy and efficiency of map inference for markov logic"

Connected through hyperedge HE-4 (induced by source paper "join graph propagation algorithms"):
  with members:
    - "a scheme for approximating probabilistic inference"
    - "expectation propagation for approximate bayesian inference"
    - "value elimination bayesian inference via backtracking search"
    - "iterative join graph propagation"
    - "an importance sampling algorithm based on evidence pre propagation"
    - "structured region graphs morphing ep into gbp"
    - "a simple insight into iterative belief propagation s success"
    - "node splitting a scheme for generating upper bounds in bayesian networks"

Question: Please tell me which class the center node 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)