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 probabilistic inference for asymmetric graphical models"):
  Abstract: Lifted probabilistic inference algorithms have been successfully applied to a large number of symmetric graphical models. Unfortunately, the majority of real-world graphical models is asymmetric. This is even the case for relational representations when evidence is given. Therefore, more recent work in the community moved to making the models symmetric and then applying existing lifted inference algorithms. However, this approach has two shortcomings. First, all existing over-symmetric approximations require a relational representation such as Markov logic networks. Second, the induced symmetries often change the distribution significantly, making the computed probabilities highly biased. We present a framework for probabilistic sampling-based inference that only uses the induced approximate symmetries to propose steps in a Metropolis-Hastings style Markov chain. The framework, therefore, leads to improved probability estimates while remaining unbiased. Experiments demonstrate that the approach outperforms existing MCMC algorithms.

Includes members:
  Member "tractability through exchangeability a new perspective on efficient probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational pooling for graph representations")
  Member "understanding the complexity of lifted inference and asymmetric weighted model counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on constrained open world probabilistic databases")
  Member "on the complexity and approximation of binary evidence in lifted inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "taming reasoning in temporal probabilistic relational models")
  Member "symmetry aware marginal density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "rockit exploiting parallelism and symmetry for map inference in statistical relational models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond markov logic efficient mining of prediction rules in large graphs")
  Member "automorphism groups of graphical models and lifted variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "markov chains on orbits of permutation groups":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lifted hybrid variational inference")

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)