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 "exact algorithms via monotone local search"):
  Abstract: We give a new general approach for designing exact exponential-time algorithms for subset problems. In a subset problem the input implicitly describes a family of sets over a universe of size n and the task is to determine whether the family contains at least one set. Our approach is based on "monotone local search", where the goal is to extend a partial solution to a solution by adding as few elements as possible. More formally, in the extension problem we are also given as input a subset X of the universe and an integer k. The task is to determine whether one can add at most k elements to X to obtain a set in the (implicitly defined) family. Our main result is that an O*(c^k) time algorithm for the extension problem immediately yields a randomized algorithm for finding a solution of any size with running time O*((2-1/c)^n). #R##N#In many cases, the extension problem can be reduced to simply finding a solution of size at most k. Furthermore, efficient algorithms for finding small solutions have been extensively studied in the field of parameterized algorithms. Directly applying these algorithms, our theorem yields in one stroke significant improvements over the best known exponential-time algorithms for several well-studied problems, including d-Hitting Set, Feedback Vertex Set, Node Unique Label Cover, and Weighted d-SAT. Our results demonstrate an interesting and very concrete connection between parameterized algorithms and exact exponential-time algorithms. #R##N#We also show how to derandomize our algorithms at the cost of a subexponential multiplicative factor in the running time. Our derandomization is based on an efficient construction of a new pseudo-random object that might be of independent interest. Finally, we extend our methods to establish new combinatorial upper bounds and develop enumeration algorithms.

Includes members:
  Member "exact algorithms for maximum independent set":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hitting topological minors is fpt")
  Member "faster algorithms to enumerate hypergraph transversals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exact algorithms via multivariate subroutines")
  Member "a faster fpt algorithm and a smaller kernel for block graph vertex deletion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a single exponential fixed parameter algorithm for distance hereditary vertex deletion")
  Member "faster exact and parameterized algorithm for feedback vertex set in tournaments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tractability of konig edge deletion problems")
  Member "on feedback vertex set new measure and new structures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hitting long directed cycles is fixed parameter tractable")
  Member "an fpt algorithm and a polynomial kernel for linear rankwidth 1 vertex deletion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "measuring what matters a hybrid approach to dynamic programming with treewidth")
  Member "unit interval editing is fixed parameter tractable":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vertex deletion problems on chordal graphs")
  Member "linear recognition of almost interval graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "subexponential parameterized algorithms and kernelization on almost chordal 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.DS(Data Structures and Algorithms)