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 "on approximate data reduction for the rural postman problem theory and experiments"):
  Abstract: Given an undirected graph with edge weights and a subset $R$ of its edges, the Rural Postman Problem (RPP) is to find a closed walk of minimum total weight containing all edges of $R$. We prove that RPP is WK[1]-complete parameterized by the number and cost $d$ of edges traversed additionally to the required ones. Thus, in particular, RPP instances cannot be polynomial-time compressed to instances of size polynomial in $d$ unless the polynomial-time hierarchy collapses. In contrast, denoting by $b\leq 2d$ the number of vertices incident to an odd number of edges of $R$ and by $c\leq d$ the number of connected components formed by the edges in $R$, we show how to reduce any RPP instance $I$ to an RPP instance $I'$ with $2b+O(c/\varepsilon)$ vertices in $O(n^3)$ time so that any $\alpha$-approximate solution for $I'$ gives an $\alpha(1+\varepsilon)$-approximate solution for $I$, for any $\alpha\geq 1$ and $\varepsilon>0$. That is, we provide a polynomial-size approximate kernelization scheme (PSAKS). We experimentally evaluate it on wide-spread benchmark data sets as well as on two real snow plowing instances from Berlin. On instances with few connected components, the number of vertices and required edges is reduced to about $50\,\%$ at a $1\,\%$ solution quality loss. We also make first steps towards a PSAKS for the parameter $c$.

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 "revisiting connected vertex cover fpt algorithms and lossy kernels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the approximate compressibility of connected vertex cover")
  Member "a parameterized approximation algorithm for the mixed and windy capacitated arc routing problem theory and experiments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterized algorithms and data reduction for the short secluded s t path problem")
  Member "polynomial kernels for weighted problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "subexponential parameterized algorithms and kernelization on almost chordal graphs")
  Member "constant factor approximations for capacitated arc routing without triangle inequality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polynomial time data reduction for weighted problems beyond additive goal functions")
  Member "fixed parameter algorithms for minimum cost edge connectivity augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimum weight connectivity augmentation for planar straight line graphs")
  Member "new inapproximability bounds for tsp":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a quasi polynomial algorithm for well spaced hyperbolic tsp")

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)