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 "symmetry aware marginal density estimation"):
  Abstract: The Rao-Blackwell theorem is utilized to analyze and improve the scalability of inference in large probabilistic models that exhibit symmetries. A novel marginal density estimator is introduced and shown both analytically and empirically to outperform standard estimators by several orders of magnitude. The developed theory and algorithms apply to a broad class of probabilistic models including statistical relational models considered not susceptible to lifted probabilistic inference.

Includes members:
  Member "rao blackwellised particle filtering for dynamic bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "poserbpf a rao blackwellized particle filter for 6d object pose tracking")
  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 "sum product networks a new deep architecture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "training deep energy based models with f divergence minimization")
  Member "lifted relational variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lifted hybrid variational inference")
  Member "markov chains on orbits of permutation groups":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lifted hybrid variational inference")
  Member "cutset sampling for bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gaussian mixture reduction for time constrained approximate inference in hybrid bayesian networks")
  Member "policy recognition in the abstract hidden markov model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "plots procedure learning from observations using subtask structure")

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)