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: "extended lifted inference with joint formulas"
  Abstract: The First-Order Variable Elimination (FOVE) algorithm allows exact inference to be applied directly to probabilistic relational models, and has proven to be vastly superior to the application of standard inference methods on a grounded propositional model. Still, FOVE operators can be applied under restricted conditions, often forcing one to resort to propositional inference. This paper aims to extend the applicability of FOVE by providing two new model conversion operators: the first and the primary is joint formula conversion and the second is just-different counting conversion. These new operations allow efficient inference methods to be applied directly on relational models, where no existing efficient method could be applied hitherto. In addition, aided by these capabilities, we show how to adapt FOVE to provide exact solutions to Maximum Expected Utility (MEU) queries over relational models for decision under uncertainty. Experimental evaluations show our algorithms to provide significant speedup over the alternatives.

Connected through hyperedge HE-1 (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-2 (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 variable elimination decoupling the operators from the constraint language"
    - "lifted inference for relational continuous models"
    - "probabilistic theorem proving"
    - "exploiting contextual independence in probabilistic inference"
    - "a knowledge compilation map"

Connected through hyperedge HE-3 (induced by source paper "lifted variable elimination decoupling the operators from the constraint language"):
  with members:
    - "bisimulation based approximate lifted inference"
    - "constraint processing in lifted probabilistic inference"
    - "lifted inference for relational continuous models"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-4 (induced by source paper "lifted variable elimination a novel operator and completeness results"):
  with members:
    - "bisimulation based approximate lifted inference"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-5 (induced by source paper "lifted relational variational inference"):
  with members:
    - "estimating continuous distributions in bayesian classifiers"
    - "spook a system for probabilistic object oriented knowledge representation"
    - "nonparametric bayesian logic"
    - "lifted inference for relational continuous models"

Connected through hyperedge HE-6 (induced by source paper "exploiting uniform assignments in first order mpe"):
  with members:
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"

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)