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 "scheduling kernels via configuration lp"):
  Abstract: Makespan minimization (on parallel identical or unrelated machines) is arguably the most natural and studied scheduling problem. A common approach in practical algorithm design is to reduce the size of a given instance by a fast preprocessing step while being able to recover key information even after this reduction. This notion is formally studied as kernelization (or simply, kernel) -- a polynomial time procedure which yields an equivalent instance whose size is bounded in terms of some given parameter. It follows from known results that makespan minimization parameterized by the longest job processing time $p_{\max}$ has a kernelization yielding a reduced instance whose size is exponential in $p_{\max}$. Can this be reduced to polynomial in $p_{\max}$? #R##N#We answer this affirmatively not only for makespan minimization, but also for the (more complicated) objective of minimizing the weighted sum of completion times, also in the setting of unrelated machines when the number of machine kinds is a parameter. Our algorithm first solves the Configuration LP and based on its solution constructs a solution of an intermediate problem, called huge $N$-fold integer programming. This solution is further reduced in size by a series of steps, until its encoding length is polynomial in the parameters. Then, we show that huge $N$-fold IP is in NP, which implies that there is a polynomial reduction back to our scheduling problem, yielding a kernel. #R##N#Our technique is highly novel in the context of kernelization, and our structural theorem about the Configuration LP is of independent interest. Moreover, we show a polynomial kernel for huge $N$-fold IP conditional on whether the so-called separation subproblem can be solved in polynomial time. Considering that integer programming does not admit polynomial kernels except for quite restricted cases, our "conditional kernel" provides new insight.

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 "polynomial time data reduction for weighted problems beyond additive goal functions")
  Member "multitype integer monoid optimization and applications":
    also connected through other hyperedges:
      (none)
  Member "a parameterized strongly polynomial algorithm for block structured integer programs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "n fold integer programming via lp rounding")
  Member "scheduling meets n fold integer programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "n fold integer programming via lp rounding")
  Member "combinatorial n fold integer programming and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "n fold integer programming via lp rounding")
  Member "interval scheduling and colorful independent sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "independent sets of dynamic rectangles algorithms and experiments")
  Member "on polynomial kernels for integer linear programs covering packing and feasibility":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polynomial kernels for weighted problems")
  Member "on polynomial kernels for sparse integer linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "covering a tree with rooted subtrees")

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)