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: "inference in hybrid networks theoretical limits and practical algorithms"
  Abstract: An important subclass of hybrid Bayesian networks are those that represent Conditional Linear Gaussian (CLG) distributions --- a distribution with a multivariate Gaussian component for each instantiation of the discrete variables. In this paper we explore the problem of inference in CLGs. We show that inference in CLGs can be significantly harder than inference in Bayes Nets. In particular, we prove that even if the CLG is restricted to an extremely simple structure of a polytree in which every continuous node has at most one discrete ancestor, the inference task is NP-hard.To deal with the often prohibitive computational cost of the exact inference algorithm for CLGs, we explore several approximate inference algorithms. These algorithms try to find a small subset of Gaussians which are a good approximation to the full mixture distribution. We consider two Monte Carlo approaches and a novel approach that enumerates mixture components in order of prior probability. We compare these methods on a variety of problems and show that our novel algorithm is very promising for large, hybrid diagnosis problems.

Connected through hyperedge HE-1 (induced by source paper "efficient online learning for optimizing value of information theory and application to interactive troubleshooting"):
  with members:
    - "near optimal bayesian active learning for decision making"
    - "using robdds for inference in bayesian networks with troubleshooting as an example"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "optimal value of information in graphical models"

Connected through hyperedge HE-2 (induced by source paper "exact inference in networks with discrete children of continuous parents"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "a variational approximation for bayesian networks with discrete and continuous latent variables"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "variational approximations between mean field theory and the junction tree algorithm"

Connected through hyperedge HE-3 (induced by source paper "inference in hybrid bayesian networks using mixtures of gaussians"):
  with members:
    - "mixtures of gaussians and minimum relative entropy techniques for modeling continuous uncertainties"
    - "nonuniform dynamic discretization in hybrid networks"
    - "a variational approximation for bayesian networks with discrete and continuous latent variables"
    - "exact inference in networks with discrete children of continuous parents"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "hybrid bayesian networks with linear deterministic variables"

Connected through hyperedge HE-4 (induced by source paper "lifted inference for relational continuous models"):
  with members:
    - "object oriented bayesian networks"
    - "spook a system for probabilistic object oriented knowledge representation"
    - "inference in hybrid bayesian networks using mixtures of gaussians"

Connected through hyperedge HE-5 (induced by source paper "expectation propogation for approximate inference in dynamic bayesian networks"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "tractable inference for complex stochastic processes"

Connected through hyperedge HE-6 (induced by source paper "substructure and boundary modeling for continuous action recognition"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "optimal value of information in graphical models"):
  with members:
    - "tractable inference for complex stochastic processes"
    - "an approximate nonmyopic computation for value of information"
    - "learning diagnostic policies from examples by systematic search"
    - "near optimal nonmyopic value of information in graphical models"
    - "complexity results and approximation strategies for map explanations"
    - "set k cover algorithms for energy efficient monitoring in wireless sensor networks"
    - "the computational complexity of probabilistic planning"
    - "active learning with statistical models"

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)