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 "strong backdoors to bounded treewidth sat"):
  Abstract: There are various approaches to exploiting "hidden structure" in instances of hard combinatorial problems to allow faster algorithms than for general unstructured or random instances. For SAT and its counting version #SAT, hidden structure has been exploited in terms of decomposability and strong backdoor sets. Decomposability can be considered in terms of the treewidth of a graph that is associated with the given CNF formula, for instance by considering clauses and variables as vertices of the graph, and making a variable adjacent with all the clauses it appears in. On the other hand, a strong backdoor set of a CNF formula is a set of variables such that each possible partial assignment to this set moves the formula into a fixed class for which (#)SAT can be solved in polynomial time. #R##N#In this paper we combine the two above approaches. In particular, we study the algorithmic question of finding a small strong backdoor set into the class W_t of CNF formulas whose associated graphs have treewidth at most t. The main results are positive: #R##N#(1) There is a cubic-time algorithm that, given a CNF formula F and two constants k,t\ge 0, either finds a strong W_t-backdoor set of size at most 2^k, or concludes that F has no strong W_t-backdoor set of size at most k. #R##N#(2) There is a cubic-time algorithm that, given a CNF formula F, computes the number of satisfying assignments of F or concludes that sb_t(F)>k, for any pair of constants k,t\ge 0. Here, sb_t(F) denotes the size of a smallest strong W_t-backdoor set of F. #R##N#The significance of our results lies in the fact that they allow us to exploit algorithmically a hidden structure in formulas that is not accessible by any one of the two approaches (decomposability, backdoors) alone. Already a backdoor size 1 on top of treewidth 1 (i.e., sb_1(F)=1) entails formulas of arbitrarily large treewidth and arbitrarily large cycle cutsets.

Includes members:
  Member "linear kernels and single exponential algorithms via protrusion decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hitting topological minors is fpt")
  Member "strong backdoors to nested satisfiability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "homomorphic hashing for sparse coefficient extraction")
  Member "backdoors to satisfaction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hunting for tractable languages for judgment aggregation")
  Member "backdoors to acyclic sat":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "complexity and approximability of parameterized max csps")
  Member "finding topological subgraphs is fixed parameter tractable":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hitting topological minors is fpt")
  Member "almost 2 sat is fixed parameter tractable":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimum bounded chains and minimum homologous chains in embedded simplicial complexes")
  Member "cutset sampling for bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gaussian mixture reduction for time constrained approximate inference in hybrid bayesian networks")

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.DS(Data Structures and Algorithms)