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 network design algorithms via hierarchical decompositions"
  Abstract: We develop a new approach for online network design and obtain improved competitive ratios for several problems. Our approach gives natural deterministic algorithms and simple analyses. At the heart of our work is a novel application of embeddings into hierarchically well-separated trees (HSTs) to the analysis of online network design algorithms --- we charge the cost of the algorithm to the cost of the optimal solution on any HST embedding of the terminals. This analysis technique is widely applicable to many problems and gives a unified framework for online network design. #R##N#In a sense, our work brings together two of the main approaches to online network design. The first uses greedy-like algorithms and analyzes them using dual-fitting. The second uses tree embeddings and results in randomized $O(\log n)$-competitive algorithms, where $n$ is the total number of vertices in the graph. Our approach uses deterministic greedy-like algorithms but analyzes them via HST embeddings of the terminals. Our proofs are simpler as we do not need to carefully construct dual solutions and we get $O(\log k)$ competitive ratios, where $k$ is the number of terminals. #R##N#In this paper, we apply our approach to obtain deterministic $O(\log k)$-competitive online algorithms for the following problems. #R##N#- Steiner network with edge duplication. Previously, only a randomized $O(\log n)$-competitive algorithm was known. #R##N#- Rent-or-buy. Previously, only deterministic $O(\log^2 k)$-competitive and randomized $O(\log k)$-competitive algorithms by Awerbuch, Azar and Bartal (2004) were known. #R##N#- Connected facility location. Previously, only a randomized $O(\log^2 k)$-competitive algorithm by San Felice, Williamson and Lee (2014) was known. #R##N#- Prize-collecting Steiner forest. We match the competitive ratio first achieved by Qian and Williamson (2011) and give a simpler analysis.

Connected through hyperedge HE-1 (induced by source paper "the power of recourse better algorithms for facility location in online and dynamic models"):
  with members:
    - "a tree structure for dynamic facility location"
    - "facility location problem in differential privacy model revisited"
    - "online facility location with deletions"
    - "online bipartite matching with amortized o log 2 n replacements"
    - "online and dynamic algorithms for set cover"
    - "efficient construction of probabilistic tree embeddings"
    - "facility location in evolving metrics"
    - "dynamic facility location via exponential clocks"

Connected through hyperedge HE-2 (induced by source paper "tight bounds for online weighted tree augmentation"):
  with members:
    - "improved approximation for tree augmentation saving by rewiring"
    - "online constrained forest and prize collecting network design"
    - "hitting sets online and unique max coloring"

Connected through hyperedge HE-3 (induced by source paper "adversarial bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "introduction to multi armed bandits"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "acceleration through optimistic no regret dynamics"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"

Connected through hyperedge HE-4 (induced by source paper "online constrained forest and prize collecting network design"):
  with members:
    - "the power of deferral maintaining a constant competitive steiner tree online"

Connected through hyperedge HE-5 (induced by source paper "timing matters online dynamics in broadcast games"):
  with members:
    - "an o lognloglogn upper bound on the price of stability for undirected shapley network design games"

Connected through hyperedge HE-6 (induced by source paper "last but not least online spanners for buy at bulk"):
  with members:
    - "balancing minimum spanning trees and shortest path trees"

Connected through hyperedge HE-7 (induced by source paper "designing networks with good equilibria under uncertainty"):
  with members:
    - "potential games are necessary to ensure pure nash equilibria in cost sharing games"
    - "improving the price of anarchy for selfish routing via coordination mechanisms"
    - "efficient coordination mechanisms for unrelated machine scheduling"
    - "inner product spaces for minsum coordination mechanisms"
    - "an o lognloglogn upper bound on the price of stability for undirected shapley network design games"

Connected through hyperedge HE-8 (induced by source paper "online balanced repartitioning"):
  with members:
    - "rent lease or buy randomized algorithms for multislope ski rental"
    - "online companion caching"
    - "competitive paging algorithms"

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)