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: "skolemization for weighted first order model counting"
  Abstract: First-order model counting emerged recently as a novel reasoning task, at the core of efficient algorithms for probabilistic logics. We present a Skolemization algorithm for model counting problems that eliminates existential quantifiers from a first-order logic theory without changing its weighted model count. For certain subsets of first-order logic, lifted model counters were shown to run in time polynomial in the number of objects in the domain of discourse, where propositional model counters require exponential time. However, these guarantees apply only to Skolem normal form theories (i.e., no existential quantifiers) as the presence of existential quantifiers reduces lifted model counters to propositional ones. Since textbook Skolemization is not sound for model counting, these restrictions precluded efficient model counting for directed models, such as probabilistic logic programs, which rely on existential quantification. Our Skolemization procedure extends the applicability of first-order model counters to these representations. Moreover, it simplifies the design of lifted model counting algorithms.

Connected through hyperedge HE-1 (induced by source paper "domain liftability of relational marginal polytopes"):
  with members:
    - "relational marginal problems theory and estimation"
    - "hardness of parameter estimation in graphical models"

Connected through hyperedge HE-2 (induced by source paper "smoothing structured decomposable circuits"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "on relaxing determinism in arithmetic circuits"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "the sum product theorem a foundation for learning tractable models"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "expander cnfs have exponential dnnf size"

Connected through hyperedge HE-3 (induced by source paper "lifted weight learning of markov logic networks revisited"):
  with members:
    - "relational marginal problems theory and estimation"
    - "entropy optimization and counting"

Connected through hyperedge HE-4 (induced by source paper "weighted model counting beyond two variable logic"):
  with members:
    - "on the uniform one dimensional fragment"
    - "new liftable classes for first order probabilistic inference"
    - "symmetric weighted first order model counting"
    - "regular graphs and the spectra of two variable logic with counting"
    - "algebraic model counting"

Connected through hyperedge HE-5 (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"
    - "lower complexity bounds for lifted inference"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "approximate lifted inference with probabilistic databases"):
  with members:
    - "linearized and single pass belief propagation"
    - "oblivious bounds on the probability of boolean functions"
    - "uncertainty in crowd data sourcing under structural constraints"
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "probabilistic databases with markoviews"
    - "formula based probabilistic inference"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"
    - "probabilistic theorem proving"

Connected through hyperedge HE-7 (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"
    - "symmetry aware marginal density estimation"
    - "probabilistic databases with markoviews"
    - "algebraic model counting"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-8 (induced by source paper "lifted variable elimination for probabilistic logic programming"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "multiplicative factorization of noisy max"
    - "lifted variable elimination decoupling the operators from the constraint language"
    - "constraint processing in lifted probabilistic inference"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "exploiting causal independence in bayesian network 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)