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: "connecting width and structure in knowledge compilation"
  Abstract: Several query evaluation tasks can be done via knowledge compilation: the query result is compiled as a lineage circuit from which the answer can be determined. For such tasks, it is important to leverage some width parameters of the circuit, such as bounded treewidth or pathwidth, to convert the circuit to structured classes, e.g., deterministic structured NNFs (d-SDNNFs) or OBDDs. In this work, we show how to connect the width of circuits to the size of their structured representation, through upper and lower bounds. For the upper bound, we show how bounded-treewidth circuits can be converted to a d-SDNNF, in time linear in the circuit size. Our bound, unlike existing results, is constructive and only singly exponential in the treewidth. We show a related lower bound on monotone DNF or CNF formulas, assuming a constant bound on the arity (size of clauses) and degree (number of occurrences of each variable). Specifically, any d-SDNNF (resp., SDNNF) for such a DNF (resp., CNF) must be of exponential size in its treewidth; and the same holds for pathwidth when compiling to OBDDs. Our lower bounds, in contrast with most previous work, apply to any formula of this class, not just a well-chosen family. Hence, for our language of DNF and CNF, pathwidth and treewidth respectively characterize the efficiency of compiling to OBDDs and (d-)SDNNFs, that is, compilation is singly exponential in the width parameter. We conclude by applying our lower bound results to the task of query evaluation.

Connected through hyperedge HE-1 (induced by source paper "graph width measures for cnf encodings with auxiliary variables"):
  with members:
    - "connecting knowledge compilation classes and width parameters"
    - "qbf as an alternative to courcelle s theorem"
    - "circuit treewidth sentential decision and query compilation"
    - "understanding model counting for beta acyclic cnf formulas"
    - "strong backdoors to bounded treewidth sat"
    - "a knowledge compilation map"

Connected through hyperedge HE-2 (induced by source paper "revisiting graph width measures for cnf encodings"):
  with members:
    - "connecting knowledge compilation classes and width parameters"
    - "qbf as an alternative to courcelle s theorem"
    - "circuit treewidth sentential decision and query compilation"
    - "model counting for formulas of bounded clique width"
    - "strong backdoors to bounded treewidth sat"
    - "a knowledge compilation map"

Connected through hyperedge HE-3 (induced by source paper "connecting knowledge compilation classes and width parameters"):
  with members:
    - "towards deterministic decomposable circuits for safe queries"
    - "on the relative succinctness of sentential decision diagrams"
    - "understanding the complexity of sat using knowledge compilation"
    - "a circuit based approach to efficient enumeration"
    - "non fpt lower bounds for structural restrictions of decision dnnf"
    - "circuit treewidth sentential decision and query compilation"
    - "tractable lineages on treelike instances limits and extensions"
    - "sdds are exponentially more succinct than obdds"

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.DB(Databases)