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: "lifted relational variational inference"
  Abstract: Hybrid continuous-discrete models naturally represent many real-world applications in robotics, finance, and environmental engineering. Inference with large-scale models is challenging because relational structures deteriorate rapidly during inference with observations. The main contribution of this paper is an efficient relational variational inference algorithm that factors largescale probability models into simpler variational models, composed of mixtures of iid (Bernoulli) random variables. The algorithm takes probability relational models of largescale hybrid systems and converts them to a close-to-optimal variational models. Then, it efficiently calculates marginal probabilities on the variational models by using a latent (or lifted) variable elimination or a lifted stochastic sampling. This inference is unique because it maintains the relational structure upon individual observations and during inference steps.

Connected through hyperedge HE-1 (induced by source paper "lifted hybrid variational inference"):
  with members:
    - "coarse to fine lifted map inference in computer vision"
    - "lifted tree reweighted variational inference"
    - "beyond log supermodularity lower bounds and the bethe partition function"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "lifted inference for relational continuous models"
    - "counting belief propagation"
    - "the bethe partition function of log supermodular graphical models"
    - "nonparametric variational inference"

Connected through hyperedge HE-2 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-3 (induced by source paper "structured learning modulo theories"):
  with members:
    - "optimization modulo theories with linear rational costs"
    - "pushing the envelope of optimization modulo theories with linear arithmetic cost functions"
    - "probabilistic similarity logic"
    - "church a language for generative models"
    - "inference in probabilistic logic programs with continuous random variables"
    - "optimization in smt with la q cost functions"
    - "lifted inference for relational continuous models"
    - "the magic of logical inference in probabilistic programming"

Connected through hyperedge HE-4 (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"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "cutset sampling for bayesian networks"
    - "policy recognition in the abstract hidden markov model"

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.LG(Machine Learning)