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: "an empirical study of w cutset sampling for bayesian networks"
  Abstract: The paper studies empirically the time-space trade-off between sampling and inference in a sl cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian network and applies exact inference over the rest. Consequently, while the size of the sampling space decreases, requiring less samples for convergence, the time for generating each single sample increases. The w-cutset sampling selects a sampling set such that the induced-width of the network when the sampling set is observed is bounded by w, thus requiring inference whose complexity is exponential in w. In this paper, we investigate performance of w-cutset sampling over a range of w values and measure the accuracy of w-cutset sampling as a function of w. Our experiments demonstrate that the cutset sampling idea is quite powerful showing that an optimal balance between inference and sampling benefits substantially from restricting the cutset size, even at the cost of more complex inference.

Connected through hyperedge HE-1 (induced by source paper "cutset sampling with likelihood weighting"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "backward simulation in bayesian networks"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "empirical evaluation of approximation algorithms for probabilistic decoding"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "an importance sampling algorithm based on evidence pre propagation"

Connected through hyperedge HE-2 (induced by source paper "approximate inference algorithms for hybrid bayesian networks with discrete constraints"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "iterative join graph propagation"
    - "on finding minimal w cutset"
    - "a simple insight into iterative belief propagation s success"
    - "mixtures of deterministic probabilistic networks and their and or search space"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "and or importance sampling"):
  with members:
    - "hugs combining exact inference and gibbs sampling in junction trees"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "on finding minimal w cutset"):
  with members:
    - "a sufficiently fast algorithm for finding close to optimal junction trees"
    - "randomized algorithms for the loop cutset problem"

Connected through hyperedge HE-5 (induced by source paper "active tuples based scheme for bounding posterior beliefs"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "large deviation methods for approximate probabilistic inference"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "knowledge engineering for large belief networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"
    - "accuracy bounds for belief propagation"
    - "approximate decomposition a method for bounding and estimating probabilistic and deterministic queries"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-6 (induced by source paper "cutset sampling for bayesian networks"):
  with members:
    - "hugs combining exact inference and gibbs sampling in junction trees"
    - "loopy belief propagation for approximate inference an empirical study"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "empirical evaluation of approximation algorithms for probabilistic decoding"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "global conditioning for probabilistic inference in belief 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)