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: "cutset sampling for bayesian networks"
  Abstract: The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structure-exploiting application of the Rao-Blackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memory-based inference algorithms. It can also be viewed as an anytime approximation of the exact cutset-conditioning algorithm developed by Pearl. Cutset sampling can be implemented efficiently when the sampled variables constitute a loop-cutset of the Bayesian network and, more generally, when the induced width of the network's graph conditioned on the observed sampled variables is bounded by a constant w. We demonstrate empirically the benefit of this scheme on a range of benchmarks.

Connected through hyperedge HE-1 (induced by source paper "gaussian mixture reduction for time constrained approximate inference in hybrid bayesian networks"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "inference in hybrid bayesian networks using mixtures of gaussians"

Connected through hyperedge HE-2 (induced by source paper "approximate knowledge compilation by online collapsed importance sampling"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "iterative join graph propagation"
    - "on bayesian network approximation by edge deletion"
    - "on the robustness of most probable explanations"

Connected through hyperedge HE-3 (induced by source paper "message passing and combinatorial optimization"):
  with members:
    - "augmentative message passing for traveling salesman problem and graph partitioning"
    - "perturbed message passing for constraint satisfaction problems"
    - "revisiting algebra and complexity of inference in graphical models"
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "message passing algorithms reparameterizations and splittings"
    - "the bethe permanent of a nonnegative matrix"

Connected through hyperedge HE-4 (induced by source paper "guarantees and limits of preprocessing in constraint satisfaction and reasoning"):
  with members:
    - "on feedback vertex set new measure and new structures"
    - "parameterized complexity and kernel bounds for hard planning problems"
    - "backdoors to satisfaction"
    - "backdoors to tractable answer set programming"
    - "limits of preprocessing"
    - "kernels for global constraints"
    - "range and roots two common patterns for specifying and propagating counting and occurrence constraints"
    - "the parameterized complexity of global constraints"

Connected through hyperedge HE-5 (induced by source paper "symmetry aware marginal density estimation"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "automorphism groups of graphical models and lifted variational inference"
    - "sum product networks a new deep architecture"
    - "lifted relational variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "policy recognition in the abstract hidden markov model"

Connected through hyperedge HE-6 (induced by source paper "strong backdoors to bounded treewidth sat"):
  with members:
    - "linear kernels and single exponential algorithms via protrusion decompositions"
    - "strong backdoors to nested satisfiability"
    - "backdoors to satisfaction"
    - "backdoors to acyclic sat"
    - "finding topological subgraphs is fixed parameter tractable"
    - "almost 2 sat is fixed parameter tractable"

Connected through hyperedge HE-7 (induced by source paper "a single exponential fpt algorithm for the k_4 minor cover problem"):
  with members:
    - "slightly superexponential parameterized problems"
    - "solving connectivity problems parameterized by treewidth in single exponential time"
    - "obtaining a bipartite graph by contracting few edges"
    - "hitting forbidden minors approximation and kernelization"

Connected through hyperedge HE-8 (induced by source paper "hybrid variational gibbs collapsed inference in topic models"):
  with members:
    - "a tutorial on learning with 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)