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: "online multistage subset maximization problems"
  Abstract: Numerous combinatorial optimization problems (knapsack, maximum-weight matching, etc.) can be expressed as \emph{subset maximization problems}: One is given a ground set $N=\{1,\dots,n\}$, a collection $\mathcal{F}\subseteq 2^N$ of subsets thereof such that $\emptyset\in\mathcal{F}$, and an objective (profit) function $p:\mathcal{F}\rightarrow\mathbb{R}_+$. The task is to choose a set $S\in\mathcal{F}$ that maximizes $p(S)$. We consider the \emph{multistage} version (Eisenstat et al., Gupta et al., both ICALP 2014) of such problems: The profit function $p_t$ (and possibly the set of feasible solutions $\mathcal{F}_t$) may change over time. Since in many applications changing the solution is costly, the task becomes to find a sequence of solutions that optimizes the trade-off between good per-time solutions and stable solutions taking into account an additional similarity bonus. As similarity measure for two consecutive solutions, we consider either the size of the intersection of the two solutions or the difference of $n$ and the Hamming distance between the two characteristic vectors. We study multistage subset maximization problems in the \emph{online} setting, that is, $p_t$ (along with possibly $\mathcal{F}_t$) only arrive one by one and, upon such an arrival, the online algorithm has to output the corresponding solution without knowledge of the future. We develop general techniques for online multistage subset maximization and thereby characterize those models (given by the type of data evolution and the type of similarity measure) that admit a constant-competitive online algorithm. When no constant competitive ratio is possible, we employ lookahead to circumvent this issue. When a constant competitive ratio is possible, we provide almost matching lower and upper bounds on the best achievable one.

Connected through hyperedge HE-1 (induced by source paper "multistage graph problems on a global budget"):
  with members:
    - "multistage vertex cover"
    - "temporal matching"
    - "computing maximum matchings in temporal graphs"
    - "multistage knapsack"
    - "changing bases multistage optimization for matroids and matchings"
    - "facility location in evolving metrics"
    - "contracting graphs to paths and trees"

Connected through hyperedge HE-2 (induced by source paper "approximating multistage matching problems"):
  with members:
    - "multistage vertex cover"
    - "temporal vertex cover with a sliding time window"
    - "fully dynamic matching in bipartite graphs"
    - "changing bases multistage optimization for matroids and matchings"

Connected through hyperedge HE-3 (induced by source paper "lp based algorithms for multistage minimization problems"):
  with members:
    - "multistage vertex cover"
    - "multistage knapsack"
    - "changing bases multistage optimization for matroids and matchings"
    - "facility location in evolving metrics"
    - "dynamic facility location via exponential clocks"
    - "the power of deferral maintaining a constant competitive steiner tree online"
    - "on the tradeoff between stability and fit"
    - "a new multilayered pcp and the hardness of hypergraph vertex cover"

Connected through hyperedge HE-4 (induced by source paper "multistage problems on a global budget"):
  with members:
    - "multistage vertex cover"
    - "multistage knapsack"
    - "changing bases multistage optimization for matroids and matchings"
    - "facility location in evolving metrics"
    - "contracting graphs to paths and trees"

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)