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 "lp based algorithms for multistage minimization problems"):
  Abstract: We consider a multistage framework introduced recently where, given a time horizon t=1,2,...,T, the input is a sequence of instances of a (static) combinatorial optimization problem I_1,I_2,...,I_T, (one for each time step), and the goal is to find a sequence of solutions S_1,S_2,...,S_T (one for each time step) reaching a tradeoff between the quality of the solutions in each time step and the stability/similarity of the solutions in consecutive time steps. For several polynomial-time solvable problems, such as Minimum Cost Perfect Matching, the multistage variant becomes hard to approximate (even for two time steps for Minimum Cost Perfect Matching). In this paper, we study the multistage variants of some important discrete minimization problems (including Minimum Cut, Vertex Cover, Set Cover, Prize-Collecting Steiner Tree, Prize-Collecting Traveling Salesman). We focus on the natural question of whether linear-programming-based methods may help in developing good approximation algorithms in this framework. We first show that Min Cut remains polytime solvable in its multistage variant, and Vertex Cover remains 2-approximable, as particular case of a more general statement which easily follows from the work of (Hochbaum, EJOR 2002) on monotone and IP2 problems. Then, we tackle other problems and for this we introduce a new two-threshold rounding scheme, tailored for multistage problems. As a first application, we show that this rounding scheme gives a 2$f$-approximation algorithm for the multistage variant of the f-Set Cover problem, where each element belongs to at most f sets. More interestingly, we are able to use our rounding scheme in order to propose a 3.53-approximation algorithm for the multistage variant of the Prize-Collecting Steiner Tree problem, and a 3.034-approximation algorithm for the multistage variant of the Prize-Collecting Traveling Salesman problem.

Includes members:
  Member "multistage vertex cover":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multistage graph problems on a global budget")
  Member "online multistage subset maximization problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multistage graph problems on a global budget")
  Member "multistage knapsack":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multistage graph problems on a global budget")
  Member "changing bases multistage optimization for matroids and matchings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multistage graph problems on a global budget")
  Member "facility location in evolving metrics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the power of recourse better algorithms for facility location in online and dynamic models")
  Member "dynamic facility location via exponential clocks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the power of recourse better algorithms for facility location in online and dynamic models")
  Member "the power of deferral maintaining a constant competitive steiner tree online":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scattering and sparse partitions and their applications")
  Member "on the tradeoff between stability and fit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online multistage subset maximization problems")

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)