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: "why is compiling lifted inference into a low level language so effective"
  Abstract: First-order knowledge compilation techniques have proven efficient for lifted inference. They compile a relational probability model into a target circuit on which many inference queries can be answered efficiently. Early methods used data structures as their target circuit. In our KR-2016 paper, we showed that compiling to a low-level program instead of a data structure offers orders of magnitude speedup, resulting in the state-of-the-art lifted inference technique. In this paper, we conduct experiments to address two questions regarding our KR-2016 results: 1- does the speedup come from more efficient compilation or more efficient reasoning with the target circuit?, and 2- why are low-level programs more efficient target circuits than data structures?

Connected through hyperedge HE-1 (induced by source paper "fusing first order knowledge compilation and the lifted junction tree algorithm"):
  with members:
    - "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"
    - "extended lifted inference with joint formulas"
    - "probabilistic theorem proving"
    - "exploiting contextual independence in probabilistic inference"
    - "a knowledge compilation map"

Connected through hyperedge HE-2 (induced by source paper "domain recursion for lifted inference with existential quantifiers"):
  with members:
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"
    - "lifted graphical models a survey"

Connected through hyperedge HE-3 (induced by source paper "new liftable classes for first order probabilistic inference"):
  with members:
    - "relational bayesian networks"
    - "markov chains on orbits of permutation groups"
    - "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)