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 "the online min sum set cover problem"):
  Abstract: We consider the online Min-Sum Set Cover (MSSC), a natural and intriguing generalization of the classical list update problem. In Online MSSC, the algorithm maintains a permutation on $n$ elements based on subsets $S_1, S_2, \ldots$ arriving online. The algorithm serves each set $S_t$ upon arrival, using its current permutation $\pi_{t}$, incurring an access cost equal to the position of the first element of $S_t$ in $\pi_{t}$. Then, the algorithm may update its permutation to $\pi_{t+1}$, incurring a moving cost equal to the Kendall tau distance of $\pi_{t}$ to $\pi_{t+1}$. The objective is to minimize the total access and moving cost for serving the entire sequence. We consider the $r$-uniform version, where each $S_t$ has cardinality $r$. List update is the special case where $r = 1$. #R##N#We obtain tight bounds on the competitive ratio of deterministic online algorithms for MSSC against a static adversary, that serves the entire sequence by a single permutation. First, we show a lower bound of $(r+1)(1-\frac{r}{n+1})$ on the competitive ratio. Then, we consider several natural generalizations of successful list update algorithms and show that they fail to achieve any interesting competitive guarantee. On the positive side, we obtain a $O(r)$-competitive deterministic algorithm using ideas from online learning and the multiplicative weight updates (MWU) algorithm. #R##N#Furthermore, we consider efficient algorithms. We propose a memoryless online algorithm, called Move-All-Equally, which is inspired by the Double Coverage algorithm for the $k$-server problem. We show that its competitive ratio is $\Omega(r^2)$ and $2^{O(\sqrt{\log n \cdot \log r})}$, and conjecture that it is $f(r)$-competitive. We also compare Move-All-Equally against the dynamic optimal solution and obtain (almost) tight bounds by showing that it is $\Omega(r \sqrt{n})$ and $O(r^{3/2} \sqrt{n})$-competitive.

Includes members:
  Member "chasing convex bodies optimally":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "stochastic online metric matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "pure entropic regularization for metrical task systems":
    also connected through other hyperedges:
      (none)
  Member "metrical task systems on trees via mirror descent and unfair gluing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understand dynamic regret with switching cost for online decision making")
  Member "weighted k server bounds via combinatorial dichotomies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "slaying hydrae improved bounds for generalized k server in uniform metrics")
  Member "competitive algorithms for generalized k server in uniform metrics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computing the chromatic number using graph decompositions via matrix rank")
  Member "a static optimality transformation with applications to planar point location":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic distribution sensitive point location")
  Member "the generalized work function algorithm is competitive for the generalized 2 server problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chasing convex bodies with linear competitive ratio")

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)