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: "sdds are exponentially more succinct than obdds"
  Abstract: Introduced by Darwiche (2011), sentential decision diagrams (SDDs) are essentially as tractable as ordered binary decision diagrams (OBDDs), but tend to be more succinct in practice. This makes SDDs a prominent representation language, with many applications in artificial intelligence and knowledge compilation. We prove that SDDs are more succinct than OBDDs also in theory, by constructing a family of boolean functions where each member has polynomial SDD size but exponential OBDD size. This exponential separation improves a quasipolynomial separation recently established by Razgon (2013), and settles an open problem in knowledge compilation.

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"
    - "new limits for knowledge compilation and applications to exact model counting"
    - "on compiling structured cnfs to obdds"
    - "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 "on the relative succinctness of sentential decision diagrams"):
  with members:
    - "a superpolynomial lower bound for the size of non deterministic complement of an unambiguous automaton"
    - "non fpt lower bounds for structural restrictions of decision dnnf"
    - "circuit treewidth sentential decision and query compilation"
    - "new limits for knowledge compilation and applications to exact model counting"
    - "on oblivious branching programs with bounded repetition that cannot efficiently compute cnfs of bounded treewidth"
    - "on the read once property of branching programs and cnfs of bounded treewidth"
    - "lower bounds for exact model counting and applications in probabilistic databases"
    - "the language of search"

Connected through hyperedge HE-4 (induced by source paper "circuit treewidth sentential decision and query compilation"):
  with members:
    - "tractable lineages on treelike instances limits and extensions"
    - "parameterized compilation lower bounds for restricted cnf formulas"
    - "new limits for knowledge compilation and applications to exact model counting"
    - "on the read once property of branching programs and cnfs of bounded treewidth"
    - "on obdds for cnfs of bounded treewidth"
    - "cliquewidth and knowledge compilation"
    - "the dichotomy of conjunctive queries on probabilistic structures"
    - "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)