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: "incremental medians via online bidding"
  Abstract: In the k-median problem we are given sets of facilities and customers, and distances between them. For a given set F of facilities, the cost of serving a customer u is the minimum distance between u and a facility in F. The goal is to find a set F of k facilities that minimizes the sum, over all customers, of their service costs.#R##N##R##N#Following the work of Mettu and Plaxton, we study the incremental medians problem, where k is not known in advance. An incremental algorithm produces a nested sequence of facility sets F1⊆F2⊆⋅⋅⋅⊆Fn, where |Fk|=k for each k. Such an algorithm is called c-cost-competitive if the cost of each Fk is at most c times the optimum k-median cost. We give improved incremental algorithms for the metric version of this problem: an 8-cost-competitive deterministic algorithm, a 2e≈5.44-cost-competitive randomized algorithm, a (24+e)-cost-competitive, polynomial-time deterministic algorithm, and a 6e+e≈16.31-cost-competitive, polynomial-time randomized algorithm.#R##N##R##N#We also consider the competitive ratio with respect to size. An algorithm is s-size-competitive if the cost of each Fk is at most the minimum cost of any set of k facilities, while the size of Fk is at most sk. We show that the optimal size-competitive ratios for this problem, in the deterministic and randomized cases, are 4 and e. For polynomial-time algorithms, we present the first polynomial-time O(log m)-size-approximation algorithm for the offline problem, as well as a polynomial-time O(log m)-size-competitive algorithm for the incremental problem.#R##N##R##N#Our upper bound proofs reduce the incremental medians problem to the following online bidding problem: faced with some unknown threshold T∈ℝ+, an algorithm must submit “bids” b∈ℝ+ until it submits a bid b≥T, paying the sum of all its bids. We present folklore algorithms for online bidding and prove that they are optimally competitive.#R##N##R##N#We extend some of the above results for incremental medians to approximately metric distance functions and to incremental fractional medians. Finally, we consider a restricted version of the incremental medians problem where k is restricted to one of two given values, for which we give a deterministic algorithm with a nearly optimal cost-competitive ratio.

Connected through hyperedge HE-1 (induced by source paper "general bounds for incremental maximization"):
  with members:
    - "robust randomized matchings"

Connected through hyperedge HE-2 (induced by source paper "approximation algorithms for stochastic k tsp"):
  with members:
    - "on the adaptivity gap of stochastic orienteering"
    - "a stochastic probing problem with applications"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"

Connected through hyperedge HE-3 (induced by source paper "online algorithms for multi level aggregation"):
  with members:
    - "approximation algorithms for the joint replenishment problem with deadlines"
    - "better approximation bounds for the joint replenishment problem"

Connected through hyperedge HE-4 (induced by source paper "online clique clustering"):
  with members:
    - "competitive strategies for online clique clustering"
    - "online correlation clustering"

Connected through hyperedge HE-5 (induced by source paper "competitive strategies for online clique clustering"):
  with members:
    - "online correlation clustering"

Connected through hyperedge HE-6 (induced by source paper "improved bounds for online preemptive matching"):
  with members:
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "buyback problem approximate matroid intersection with cancellation costs"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "weighted matching in the semi streaming model"

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)