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: "lower complexity bounds for lifted inference"
  Abstract: One of the big challenges in the development of probabilistic relational (or probabilistic logical) modeling and learning frameworks is the design of inference techniques that operate on the level of the abstract model representation language, rather than on the level of ground, propositional instances of the model. Numerous approaches for such "lifted inference" techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established early on in (Jaeger 2000). However, it is not immediate that these results also apply to the type of modeling languages that currently receive the most attention, i.e., weighted, quantifier-free formulas. In this paper we extend these earlier results, and show that under the assumption that NETIME =/= ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier- and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference, and for knowledge bases not containing the equality predicate.

Connected through hyperedge HE-1 (induced by source paper "the complexity of bayesian networks specified by propositional and relational languages"):
  with members:
    - "on the semantics and complexity of probabilistic logic programs"
    - "new liftable classes for first order probabilistic inference"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "query dags a practical paradigm for implementing belief network inference"
    - "a simple fptas for counting edge covers"
    - "symmetric weighted first order model counting"

Connected through hyperedge HE-2 (induced by source paper "symmetric weighted first order model counting"):
  with members:
    - "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"
    - "symmetry aware marginal density estimation"
    - "probabilistic databases with markoviews"
    - "algebraic model counting"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-3 (induced by source paper "skolemization for weighted first order model counting"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "context specific independence in bayesian networks"
    - "probabilistic databases with markoviews"
    - "algebraic model counting"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "probabilistic theorem proving"

Connected through hyperedge HE-4 (induced by source paper "markov chains on orbits of permutation groups"):
  with members:
    - "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"
    - "bisimulation based approximate lifted inference"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "symmetry breaking answer set solving"

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)