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 "connecting knowledge compilation classes and width parameters"):
  Abstract: The field of knowledge compilation establishes the tractability of many tasks by studying how to compile them to Boolean circuit classes obeying some requirements such as structuredness, decomposability, and determinism. However, in other settings such as intensional query evaluation on databases, we obtain Boolean circuits that satisfy some width bounds, e.g., they have bounded treewidth or pathwidth. In this work, we give a systematic picture of many circuit classes considered in knowledge compilation and show how they can be systematically connected to width measures, through upper and lower bounds. Our upper bounds show that bounded-treewidth circuits can be constructively converted to d-SDNNFs, in time linear in the circuit size and singly exponential in the treewidth; and that bounded-pathwidth circuits can similarly be converted to uOBDDs. We show matching lower bounds on the compilation of monotone DNF or CNF formulas to structured targets, assuming a constant bound on the arity (size of clauses) and degree (number of occurrences of each variable): any d-SDNNF (resp., SDNNF) for such a DNF (resp., CNF) must be of exponential size in its treewidth, and the same holds for uOBDDs (resp., n-OBDDs) when considering pathwidth. Unlike most previous work, our bounds apply to any formula of this class, not just a well-chosen family. Hence, we show that pathwidth and treewidth respectively characterize the efficiency of compiling monotone DNFs to uOBDDs and d-SDNNFs with compilation being singly exponential in the corresponding width parameter. We also show that our lower bounds on CNFs extend to unstructured compilation targets, with an exponential lower bound in the treewidth (resp., pathwidth) when compiling monotone CNFs of constant arity and degree to DNNFs (resp., nFBDDs).

Includes members:
  Member "towards deterministic decomposable circuits for safe queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving a special case of the intensional vs extensional conjecture in probabilistic databases")
  Member "on the relative succinctness of sentential decision diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the relation between structured d dnnfs and sdds")
  Member "connecting width and structure in knowledge compilation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph width measures for cnf encodings with auxiliary variables")
  Member "understanding the complexity of sat using knowledge compilation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "point width and max csps")
  Member "a circuit based approach to efficient enumeration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient logspace classes for enumeration counting and uniform generation")
  Member "non fpt lower bounds for structural restrictions of decision dnnf":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the relative succinctness of sentential decision diagrams")
  Member "circuit treewidth sentential decision and query compilation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the relation between structured d dnnfs and sdds")
  Member "tractable lineages on treelike instances limits and extensions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a dichotomy for homomorphism closed queries on probabilistic graphs")

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)