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 variable elimination decoupling the operators from the constraint language"
  Abstract: Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform inference more efficiently. More specifically, they identify groups of interchangeable variables and perform inference once per group, as opposed to once per variable. The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language. Existing approaches for exact lifted inference use specific languages for (in)equality constraints, which often have limited expressivity. In this article, we decouple lifted inference from the constraint language. We define operators for lifted inference in terms of relational algebra operators, so that they operate on the semantic level (the constraints' extension) rather than on the syntactic level, making them language-independent. As a result, lifted inference can be performed using more powerful constraint languages, which provide more opportunities for lifting. We empirically demonstrate that this can improve inference efficiency by orders of magnitude, allowing exact inference where until now only approximate inference was feasible.

Connected through hyperedge HE-1 (induced by source paper "exploring unknown universes in probabilistic relational models"):
  with members:
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "adaptive inference on general graphical models"
    - "probabilistic theorem proving"

Connected through hyperedge HE-2 (induced by source paper "taming reasoning in temporal probabilistic relational models"):
  with members:
    - "lifted probabilistic inference for asymmetric graphical models"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "tractable inference for complex stochastic processes"

Connected through hyperedge HE-3 (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-4 (induced by source paper "fusing first order knowledge compilation and the lifted junction tree algorithm"):
  with members:
    - "why is compiling lifted inference into a low level language so effective"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted variable elimination for probabilistic logic programming"
    - "lifted inference for relational continuous models"
    - "extended lifted inference with joint formulas"
    - "probabilistic theorem proving"
    - "exploiting contextual independence in probabilistic inference"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "answering hindsight queries with lifted dynamic junction trees"):
  with members:
    - "exact inference techniques for the analysis of bayesian attack graphs"
    - "first order decomposition trees"
    - "the factored frontier algorithm for approximate inference in dbns"

Connected through hyperedge HE-6 (induced by source paper "preventing unnecessary groundings in the lifted dynamic junction tree algorithm"):
  with members:
    - "first order decomposition trees"
    - "the factored frontier algorithm for approximate inference in dbns"

Connected through hyperedge HE-7 (induced by source paper "inference in probabilistic logic programs using lifted explanations"):
  with members:
    - "lifted variable elimination for probabilistic logic programming"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "the octagon abstract domain"

Connected through hyperedge HE-8 (induced by source paper "exploiting anonymity in approximate linear programming scaling to large multiagent mdps extended version"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "efficient solution algorithms for factored mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

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)