human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "symmetric weighted first order model counting"):
  Abstract: The FO Model Counting problem (FOMC) is the following: given a sentence $\Phi$ in FO and a number $n$, compute the number of models of $\Phi$ over a domain of size $n$; the Weighted variant (WFOMC) generalizes the problem by associating a weight to each tuple and defining the weight of a model to be the product of weights of its tuples. In this paper we study the complexity of the symmetric WFOMC, where all tuples of a given relation have the same weight. Our motivation comes from an important application, inference in Knowledge Bases with soft constraints, like Markov Logic Networks, but the problem is also of independent theoretical interest. We study both the data complexity, and the combined complexity of FOMC and WFOMC. For the data complexity we prove the existence of an FO$^{3}$ formula for which FOMC is #P$_1$-complete, and the existence of a Conjunctive Query for which WFOMC is #P$_1$-complete. We also prove that all $\gamma$-acyclic queries have polynomial time data complexity. For the combined complexity, we prove that, for every fragment FO$^{k}$, $k\geq 2$, the combined complexity of FOMC (or WFOMC) is #P-complete.

Includes members:
  Member "lower complexity bounds for lifted inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the complexity of bayesian networks specified by propositional and relational languages")
  Member "understanding the complexity of lifted inference and asymmetric weighted model counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on constrained open world probabilistic databases")
  Member "on the complexity and approximation of binary evidence in lifted inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "taming reasoning in temporal probabilistic relational models")
  Member "skolemization for weighted first order model counting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain liftability of relational marginal polytopes")
  Member "symmetry aware marginal density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generating and sampling orbits for lifted probabilistic inference")
  Member "probabilistic databases with markoviews":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "uniform reliability of self join free conjunctive queries")
  Member "algebraic model counting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lifted hybrid variational inference")

Question: Please tell me which class the source paper 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.DB(Databases)