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 "first order decomposition trees"):
  Abstract: Lifting attempts to speed up probabilistic inference by exploiting symmetries in the model. Exact lifted inference methods, like their propositional counterparts, work by recursively decomposing the model and the problem. In the propositional case, there exist formal structures, such as decomposition trees (dtrees), that represent such a decomposition and allow us to determine the complexity of inference a priori. However, there is currently no equivalent structure nor analogous complexity results for lifted inference. In this paper, we introduce FO-dtrees, which upgrade propositional dtrees to the first-order level. We show how these trees can characterize a lifted inference solution for a probabilistic logical model (in terms of a sequence of lifted operations), and make a theoretical analysis of the complexity of lifted inference in terms of the novel notion of lifted width for the tree.

Includes members:
  Member "markov chains on orbits of permutation groups":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lifted hybrid variational inference")
  Member "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "probabilistic theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")
  Member "towards completely lifted search based probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "solving sat and bayesian inference with backtracking search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")
  Member "exploiting contextual independence in probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)