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 hybrid variational inference"):
  Abstract: A variety of lifted inference algorithms, which exploit model symmetry to reduce computational cost, have been proposed to render inference tractable in probabilistic relational models. Most existing lifted inference algorithms operate only over discrete domains or continuous domains with restricted potential functions, e.g., Gaussian. We investigate two approximate lifted variational approaches that are applicable to hybrid domains and expressive enough to capture multi-modality. We demonstrate that the proposed variational methods are both scalable and can take advantage of approximate model symmetries, even in the presence of a large amount of continuous evidence. We demonstrate that our approach compares favorably against existing message-passing based approaches in a variety of settings. Finally, we present a sufficient condition for the Bethe approximation to yield a non-trivial estimate over the marginal polytope.

Includes members:
  Member "coarse to fine lifted map inference in computer vision":
    also connected through other hyperedges:
      (none)
  Member "lifted tree reweighted variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "beyond log supermodularity lower bounds and the bethe partition function":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structure learning using forced pruning")
  Member "belief optimization for binary networks a stable alternative to loopy belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "amortized bethe free energy minimization for learning mrfs")
  Member "lifted relational variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "lifted inference for relational continuous models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning distributional programs for relational autocompletion")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "the bethe partition function of log supermodular graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast convergence of belief propagation to global optima beyond correlation decay")

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.LG(Machine Learning)