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: "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.

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"
    - "symmetry aware marginal density estimation"
    - "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 "taming reasoning in temporal probabilistic relational models"):
  with members:
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "tractable inference for complex stochastic processes"
    - "lifted variable elimination decoupling the operators from the constraint language"

Connected through hyperedge HE-3 (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 tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"
    - "object oriented bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "fusing first order knowledge compilation and the lifted junction tree algorithm"):
  with members:
    - "why is compiling lifted inference into a low level language so effective"
    - "lifted variable elimination for probabilistic logic programming"
    - "lifted variable elimination decoupling the operators from the constraint language"
    - "lifted inference for relational continuous models"
    - "extended lifted inference with joint formulas"
    - "probabilistic theorem proving"
    - "exploiting contextual independence in probabilistic inference"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "coarse to fine lifted map inference in computer vision"):
  with members:
    - "learning tractable probabilistic models for fault localization"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "lifted graphical models a survey"

Connected through hyperedge HE-6 (induced by source paper "contextual symmetries in probabilistic graphical models"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "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"
    - "lifted graphical models a survey"

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)