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: "bisimulation based approximate lifted inference"
  Abstract: There has been a great deal of recent interest in methods for performing lifted inference; however, most of this work assumes that the first-order model is given as input to the system. Here, we describe lifted inference algorithms that determine symmetries and automatically lift the probabilistic model to speedup inference. In particular, we describe approximate lifted inference techniques that allow the user to trade off inference accuracy for computational efficiency by using a handful of tunable parameters, while keeping the error bounded. Our algorithms are closely related to the graph-theoretic concept of bisimulation. We report experiments on both synthetic and real data to show that in the presence of symmetries, run-times for inference can be improved significantly, with approximate lifted inference providing orders of magnitude speedup over ground inference.

Connected through hyperedge HE-1 (induced by source paper "abstracting probabilistic models a logical perspective"):
  with members:
    - "state space abstractions for probabilistic inference a systematic review"
    - "explaining transition systems through program induction"
    - "probabilistic program abstractions"
    - "symmetric weighted first order model counting"
    - "context specific independence in bayesian networks"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "markov logic in infinite domains"
    - "solving sat and bayesian inference with backtracking search"

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"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-3 (induced by source paper "abstracting probabilistic relational models"):
  with members:
    - "state space abstractions for probabilistic inference a systematic review"
    - "explaining transition systems through program induction"
    - "solving sat and bayesian inference with backtracking search"
    - "representation dependence in probabilistic inference"
    - "an abstract monte carlo method for the analysis of probabilistic programs"

Connected through hyperedge HE-4 (induced by source paper "lower complexity bounds for lifted inference"):
  with members:
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "relational bayesian networks"
    - "discriminative probabilistic models for relational data"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "probabilistic theorem proving"

Connected through hyperedge HE-5 (induced by source paper "lifted variable elimination decoupling the operators from the constraint language"):
  with members:
    - "constraint processing in lifted probabilistic inference"
    - "lifted inference for relational continuous models"
    - "counting belief propagation"
    - "extended lifted inference with joint formulas"
    - "probabilistic theorem proving"

Connected through hyperedge HE-6 (induced by source paper "markov chains on orbits of permutation groups"):
  with members:
    - "lower complexity bounds for lifted inference"
    - "practical graph isomorphism ii"
    - "general purpose mcmc inference over relational structures"
    - "a delayed column generation strategy for exact k bounded map inference in markov logic networks"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "symmetry breaking answer set solving"

Connected through hyperedge HE-7 (induced by source paper "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"):
  with members:
    - "approximating the partition function by deleting and then correcting for model edges"
    - "iterative join graph propagation"
    - "node splitting a scheme for generating upper bounds in bayesian networks"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"

Connected through hyperedge HE-8 (induced by source paper "lifted variable elimination a novel operator and completeness results"):
  with members:
    - "counting belief propagation"
    - "extended lifted inference with joint formulas"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"
    - "exploiting contextual independence in probabilistic inference"

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)