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 "polynomial time data reduction for weighted problems beyond additive goal functions"):
  Abstract: Kernelization is the fundamental notion for polynomial-time data reduction with performance guarantees. Kernelization for weighted problems particularly requires to also shrink weights. Marx and V\'egh [ACM Trans. Algorithms 2015] and Etscheid et al. [J. Comput. Syst. Sci. 2017] used a technique of Frank and Tardos [Combinatorica 1987] to obtain polynomial-size kernels for weighted problems, mostly with additive goal functions. We lift the technique to linearizable functions, a function type that we introduce and that also contains non-additive functions. Using the lifted technique, we obtain kernelization results for natural problems in graph partitioning, network design, facility location, scheduling, vehicle routing, and computational social choice, thereby improving and generalizing results from the literature.

Includes members:
  Member "parameterized algorithms and data reduction for the short secluded s t path problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scheduling kernels via configuration lp")
  Member "fixed parameter algorithms for maximum profit facility location under matroid constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the computational complexity of finding temporal paths under waiting time constraints")
  Member "on approximate data reduction for the rural postman problem theory and experiments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal size problem kernels for d hitting set in linear time and space")
  Member "fair knapsack":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "developments in multi agent fair allocation")
  Member "parameterized power vertex cover":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structural parameters tight bounds and approximation for k r center")
  Member "a parameterized approximation algorithm for the mixed and windy capacitated arc routing problem theory and experiments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parameterized algorithms and data reduction for the short secluded s t path problem")
  Member "parameterized algorithms for power efficient connected symmetric wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parameterized algorithms for power efficiently connecting sensor networks theory and experiments")
  Member "new algorithms for minimizing the weighted number of tardy jobs on a single machine":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "single machine batch scheduling to minimize the weighted number of tardy jobs")

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)