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 inference for relational continuous models"
  Abstract: Relational Continuous Models (RCMs) represent joint probability densities over attributes of objects, when the attributes have continuous domains. With relational representations, they can model joint probability distributions over large numbers of variables compactly in a natural way. This paper presents a new exact lifted inference algorithm for RCMs, thus it scales up to large models of real world applications. The algorithm applies to Relational Pairwise Models which are (relational) products of potentials of arity 2. Our algorithm is unique in two ways. First, it substantially improves the efficiency of lifted inference with variables of continuous domains. When a relational model has Gaussian potentials, it takes only linear-time compared to cubic time of previous methods. Second, it is the first exact inference algorithm which handles RCMs in a lifted way. The algorithm is illustrated over an example from econometrics. Experimental results show that our algorithm outperforms both a groundlevel inference algorithm and an algorithm built with previously-known lifted methods.

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 relational variational inference"
    - "counting belief propagation"
    - "the bethe partition function of log supermodular graphical models"
    - "nonparametric variational inference"

Connected through hyperedge HE-2 (induced by source paper "learning distributional programs for relational autocompletion"):
  with members:
    - "learning probabilistic logic programs in continuous domains"
    - "bayes ball the rational pastime for determining irrelevance and requisite information in belief networks and influence diagrams"
    - "context specific independence in bayesian networks"
    - "discriminative probabilistic models for relational data"
    - "say em for selecting probabilistic models for logical sequences"

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 variable elimination decoupling the operators from the constraint language"
    - "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 "discrete continuous mixtures in probabilistic programming generalized semantics and inference algorithms"):
  with members:
    - "estimating mutual information for discrete continuous mixtures"
    - "design and implementation of probabilistic programming language anglican"
    - "inference compilation and universal probabilistic programming"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "the magic of logical inference in probabilistic programming"

Connected through hyperedge HE-6 (induced by source paper "the automatic statistician a relational perspective"):
  with members:
    - "lifted graphical models a survey"

Connected through hyperedge HE-7 (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"
    - "lifted relational variational inference"
    - "optimization in smt with la q cost functions"
    - "the magic of logical inference in probabilistic programming"

Connected through hyperedge HE-8 (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"
    - "counting belief propagation"
    - "extended lifted inference with joint formulas"
    - "probabilistic theorem proving"

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)