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 "lifted region based belief propagation"):
  Abstract: Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Relational Models (SRMs), such as Lifted First-Order Belief Propagation. FOBP simulates propositional factor graph belief propagation without constructing the ground factor graph by identifying and lifting over redundant message computations. In this work, we propose a generalization of FOBP called Lifted Generalized Belief Propagation, in which both the region structure and the message structure can be lifted. This approach allows more of the inference to be performed intra-region (in the exact inference step of BP), thereby allowing simulation of propagation on a graph structure with larger region scopes and fewer edges, while still maintaining tractability. We demonstrate that the resulting algorithm converges in fewer iterations to more accurate results on a variety of SRMs.

Includes members:
  Member "lifted tree reweighted variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lifted hybrid variational inference")
  Member "automorphism groups of graphical models and lifted variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "template based inference in symmetric relational markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "probabilistic theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")

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)