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: "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
  Abstract: In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of constraints. We present two approximate inference algorithms for HMNs that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling and Constraint Propagation to address the complexity of modeling and reasoning in HMNs. We demonstrate the performance of our approximate inference algorithms on randomly generated HMNs.

Connected through hyperedge HE-1 (induced by source paper "on the approximability of weighted model integration on dnf structures"):
  with members:
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "on hashing based approaches to approximate dnf counting"
    - "geodesic walks in polytopes"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "distribution aware sampling and weighted model counting for sat"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "probabilistic theorem proving"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

Connected through hyperedge HE-2 (induced by source paper "modeling transportation routines using hybrid dynamic mixed networks"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "hybrid processing of beliefs and constraints"
    - "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 "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"
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "exact inference in networks with discrete children of continuous parents"
    - "hybrid bayesian networks with linear deterministic variables"

Connected through hyperedge HE-4 (induced by source paper "measuring the hardness of stochastic sampling on bayesian networks with deterministic causalities the k test"):
  with members:
    - "knowledge engineering for large belief networks"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "an importance sampling algorithm based on evidence pre propagation"
    - "mixtures of deterministic probabilistic networks and their and or search space"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "and or importance sampling"):
  with members:
    - "hugs combining exact inference and gibbs sampling in junction trees"
    - "an empirical study of w cutset sampling for bayesian networks"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "formula based probabilistic inference"):
  with members:
    - "context specific independence in bayesian networks"
    - "value elimination bayesian inference via backtracking search"
    - "and or importance sampling"

Connected through hyperedge HE-7 (induced by source paper "studies in lower bounding probabilities of evidence using the markov inequality"):
  with members:
    - "knowledge engineering for large belief networks"
    - "mixtures of deterministic probabilistic networks and their and or search space"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large 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)