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 "graph width measures for cnf encodings with auxiliary variables"):
  Abstract: We consider bounded width CNF-formulas where the width is measured by popular graph width measures on graphs associated to CNF-formulas. Such restricted graph classes, in particular those of bounded treewidth, have been extensively studied for their uses in the design of algorithms for various computational problems on CNF-formulas. Here we consider the expressivity of these formulas in the model of clausal encodings with auxiliary variables. We first show that bounding the width for many of the measures from the literature leads to a dramatic loss of expressivity, restricting the formulas to such of low communication complexity. We then show that the width of optimal encodings with respect to different measures is strongly linked: there are two classes of width measures, one containing primal treewidth and the other incidence cliquewidth, such that in each class the width of optimal encodings only differs by constant factors. Moreover, between the two classes the width differs at most by a factor logarithmic in the number of variables. Both these results are in stark contrast to the setting without auxiliary variables where all width measures we consider here differ by more than constant factors and in many cases even by linear factors.

Includes members:
  Member "connecting knowledge compilation classes and width parameters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient logspace classes for enumeration counting and uniform generation")
  Member "qbf as an alternative to courcelle s theorem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "te eth lower bounds for qbfs of bounded treewidth")
  Member "connecting width and structure in knowledge compilation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting graph width measures for cnf encodings")
  Member "circuit treewidth sentential decision and query compilation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the relation between structured d dnnfs and sdds")
  Member "understanding model counting for beta acyclic cnf formulas":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximate counting csp seen from the other side")
  Member "strong backdoors to bounded treewidth sat":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting graph width measures for cnf encodings")
  Member "a knowledge compilation map":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abstracting probabilistic models a logical perspective")

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.CC(Computational Complexity)