human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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 $c$.

Connected through hyperedge HE-1 (induced by source paper "optimal size problem kernels for d hitting set in linear time and space"):
  with members:
    - "understanding the effectiveness of data reduction in public transportation networks"
    - "computing hitting set kernels by ac 0 circuits"
    - "efficiently enumerating hitting sets of hypergraphs arising in data profiling"
    - "exploiting hidden structure in selecting dimensions that distinguish vectors"
    - "a shortcut to sun flowers kernels in logarithmic space or linear time"
    - "towards optimal and expressive kernelization for d hitting set"

Connected through hyperedge HE-2 (induced by source paper "polynomial time data reduction for weighted problems beyond additive goal functions"):
  with members:
    - "parameterized algorithms and data reduction for the short secluded s t path problem"
    - "fixed parameter algorithms for maximum profit facility location under matroid constraints"
    - "fair knapsack"
    - "parameterized power vertex cover"
    - "a parameterized approximation algorithm for the mixed and windy capacitated arc routing problem theory and experiments"
    - "parameterized algorithms for power efficient connected symmetric wireless sensor networks"
    - "new algorithms for minimizing the weighted number of tardy jobs on a single machine"
    - "interval scheduling and colorful independent sets"

Connected through hyperedge HE-3 (induced by source paper "parameterized algorithms for power efficiently connecting sensor networks theory and experiments"):
  with members:
    - "parameterizing edge modification problems above lower bounds"
    - "a parameterized approximation algorithm for the mixed and windy capacitated arc routing problem theory and experiments"
    - "parameterized algorithms for power efficient connected symmetric wireless sensor networks"
    - "minimum power range assignment for symmetric connectivity in sensor networks with two power levels"
    - "raising the bar for vertex cover fixed parameter tractability above a higher guarantee"
    - "on multiway cut parameterized above lower bounds"

Question: Please tell me which class the center node 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)