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: "metrical task systems on trees via mirror descent and unfair gluing"
  Abstract: We consider metrical task systems on tree metrics, and present an $O(\mathrm{depth} \times \log n)$-competitive randomized algorithm based on the mirror descent framework introduced in our prior work on the $k$-server problem. For the special case of hierarchically separated trees (HSTs), we use mirror descent to refine the standard approach based on gluing unfair metrical task systems. This yields an $O(\log n)$-competitive algorithm for HSTs, thus removing an extraneous $\log\log n$ in the bound of Fiat and Mendel (2003). Combined with well-known HST embedding theorems, this also gives an $O((\log n)^2)$-competitive randomized algorithm for every $n$-point metric space.

Connected through hyperedge HE-1 (induced by source paper "the online min sum set cover problem"):
  with members:
    - "chasing convex bodies optimally"
    - "stochastic online metric matching"
    - "pure entropic regularization for metrical task systems"
    - "weighted k server bounds via combinatorial dichotomies"
    - "competitive algorithms for generalized k server in uniform metrics"
    - "a static optimality transformation with applications to planar point location"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"
    - "a note on the generalized min sum set cover problem"

Connected through hyperedge HE-2 (induced by source paper "understand dynamic regret with switching cost for online decision making"):
  with members:
    - "introduction to online convex optimization"
    - "proximal online gradient is optimum for dynamic regret"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "adaptive online learning in dynamic environments"
    - "dynamic regret of strongly adaptive methods"
    - "fusible hsts and the randomized k server conjecture"
    - "k server via multiscale entropic regularization"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"

Connected through hyperedge HE-3 (induced by source paper "parametrized metrical task systems"):
  with members:
    - "metrical service systems with multiple servers"
    - "better algorithms for unfair metrical task systems and applications"
    - "competitive paging algorithms"

Connected through hyperedge HE-4 (induced by source paper "pure entropic regularization for metrical task systems"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "better algorithms for unfair metrical task systems and applications"

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)