human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fusing first order knowledge compilation and the lifted junction tree algorithm"):
  Abstract: Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries as well as first-order knowledge compilation (FOKC) based on weighted model counting. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a model and LVE as a subroutine in its computations. For certain inputs, the implementations of LVE and, as a result, LJT ground parts of a model where FOKC has a lifted run. The purpose of this paper is to prepare LJT as a backbone for lifted inference and to use any exact inference algorithm as subroutine. Using FOKC in LJT allows us to compute answers faster than LJT, LVE, and FOKC for certain inputs.

Includes members:
  Member "why is compiling lifted inference into a low level language so effective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain recursion for lifted inference with existential quantifiers")
  Member "lifted probabilistic inference for asymmetric graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "lifted variable elimination for probabilistic logic programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constraint based inference in probabilistic logic programs")
  Member "lifted variable elimination decoupling the operators from the constraint language":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploring unknown universes in probabilistic relational models")
  Member "lifted inference for relational continuous models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lifted hybrid variational inference")
  Member "extended lifted inference with joint formulas":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "probabilistic theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")
  Member "exploiting contextual independence in probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "addmc exact weighted model counting with algebraic decision diagrams")

Question: Please tell me which class the source paper 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)