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 "a faster fptas for knapsack problem with cardinality constraint"):
  Abstract: We study the $K$-item knapsack problem (\ie, $1.5$-dimensional KP), which is a generalization of the famous 0-1 knapsack problem (\ie, $1$-dimensional KP) in which an upper bound $K$ is imposed on the number of items selected. This problem is of fundamental importance and is known to have a broad range of applications in various fields such as computer science and operation research. It is well known that, there is no FPTAS for the $d$-dimensional knapsack problem when $d\geq 2$, unless P $=$ NP. While the $K$-item knapsack problem is known to admit an FPTAS, the complexity of all existing FPTASs have a high dependency on the cardinality bound $K$ and approximation error $\varepsilon$, which could result in inefficiencies especially when $K$ and $\varepsilon^{-1}$ increase. The current best results are due to \cite{mastrolilli2006hybrid}, in which two schemes are presented exhibiting a space-time tradeoff--one scheme with time complexity $O(n+Kz^{2}/\varepsilon^{2})$ and space complexity $O(n+z^{3}/\varepsilon)$, while another scheme requires $O(n+(Kz^{2}+z^{4})/\varepsilon^{2})$ run-time but only needs $O(n+z^{2}/\varepsilon)$ space, where $z=\min\{K,1/\varepsilon\}$. In this paper we close the space-time tradeoff exhibited in \cite{mastrolilli2006hybrid} by designing a new FPTAS with a run-time of $\widetilde{O}(n+z^{2}/\varepsilon^{2})$, while simultaneously reaching the $O(n+z^{2}/\varepsilon)$ space bound. Our scheme provides $\widetilde{O}(K)$ and $O(z)$ improvements on the long-established state-of-the-art algorithms in time and space complexity respectively, and is the first scheme that achieves a run-time that is asymptotically independent of cardinality bound $K$ under fixed $\varepsilon$. Another salient feature of our scheme is that it is the first FPTAS, which achieves better time and space complexity bounds than the very first standard FPTAS over all parameter regimes.

Includes members:
  Member "a faster fptas for the unbounded knapsack problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on problems equivalent to min convolution")
  Member "curvature and optimal algorithms for learning and minimizing submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "optimal source based filtering of malicious traffic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding the detection of view fraud in video content portals")
  Member "bin packing with general cost structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multitype integer monoid optimization and applications")
  Member "submodular approximation sampling based algorithms and lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "hybrid rounding techniques for knapsack problems":
    also connected through other hyperedges:
      (none)

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)