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 for probabilistic logic programming"
  Abstract: Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.

Connected through hyperedge HE-1 (induced by source paper "constraint based inference in probabilistic logic programs"):
  with members:
    - "inference in probabilistic logic programs using lifted explanations"
    - "new liftable classes for first order probabilistic inference"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "stochastic logic programs sampling inference and applications"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "logical hidden markov models"

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 decoupling the operators from the constraint language"
    - "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-3 (induced by source paper "inference in probabilistic logic programs using lifted explanations"):
  with members:
    - "lifted variable elimination decoupling the operators from the constraint language"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "the octagon abstract domain"

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)