human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "generating and sampling orbits for lifted probabilistic inference"):
  with members:
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "practical graph isomorphism ii"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-2 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "object oriented bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "symmetric weighted first order model counting"):
  with members:
    - "lower complexity bounds for lifted inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "skolemization for weighted first order model counting"
    - "probabilistic databases with markoviews"
    - "algebraic model counting"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-4 (induced by source paper "tractability through exchangeability a new perspective on efficient probabilistic inference"):
  with members:
    - "exchangeable variable models"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "context specific independence in bayesian networks"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "probabilistic theorem proving"

Connected through hyperedge HE-5 (induced by source paper "lifted probabilistic inference for asymmetric graphical models"):
  with members:
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"

Connected through hyperedge HE-6 (induced by source paper "on the complexity and approximation of binary evidence in lifted inference"):
  with members:
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Question: Please tell me which class the center node 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)