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: "on compiling structured cnfs to obdds"
  Abstract: We present new results on the size of OBDD representations of structurally characterized classes of CNF formulas. First, we identify a natural sufficient condition, which we call the few subterms property, for a class of CNFs to have polynomial OBDD size; we then prove that CNFs whose incidence graphs are variable convex have few subterms (and hence have polynomial OBDD size), and observe that the few subterms property also explains the known fact that classes of CNFs of bounded treewidth have polynomial OBDD size. Second, we prove an exponential lower bound on the OBDD size of a family of CNF classes with incidence graphs of bounded degree, exploiting the combinatorial properties of expander graphs.

Connected through hyperedge HE-1 (induced by source paper "on the relation between structured d dnnfs and sdds"):
  with members:
    - "smoothing structured decomposable circuits"
    - "on the relative succinctness of sentential decision diagrams"
    - "circuit treewidth sentential decision and query compilation"
    - "sdds are exponentially more succinct than obdds"
    - "new limits for knowledge compilation and applications to exact model counting"
    - "lower bounds for exact model counting and applications in probabilistic databases"
    - "a knowledge compilation map"

Connected through hyperedge HE-2 (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"
    - "connecting width and structure in knowledge compilation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "expander cnfs have exponential dnnf size"):
  with members:
    - "no small nondeterministic read once branching programs for cnfs of bounded treewidth"
    - "on the read once property of branching programs and cnfs of bounded treewidth"
    - "on obdds for cnfs of bounded treewidth"
    - "lower bounds for exact model counting and applications in probabilistic databases"
    - "a knowledge compilation map"

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.LO(Logic in Computer Science)