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: "thresholded covering algorithms for robust and max min optimization"
  Abstract: The general problem of robust optimization is this: one of several possible scenarios will appear tomorrow, but things are more expensive tomorrow than they are today. What should you anticipatorily buy today, so that the worst-case cost (summed over both days) is minimized? Feige et al. and Khandekar et al. considered the k-robust model where the possible outcomes tomorrow are given by all demand-subsets of size k, and gave algorithms for the set cover problem, and the Steiner tree and facility location problems in this model, respectively. #R##N#In this paper, we give the following simple and intuitive template for k-robust problems: "having built some anticipatory solution, if there exists a single demand whose augmentation cost is larger than some threshold, augment the anticipatory solution to cover this demand as well, and repeat". In this paper we show that this template gives us improved approximation algorithms for k-robust Steiner tree and set cover, and the first approximation algorithms for k-robust Steiner forest, minimum-cut and multicut. All our approximation ratios (except for multicut) are almost best possible. #R##N#As a by-product of our techniques, we also get algorithms for max-min problems of the form: "given a covering problem instance, which k of the elements are costliest to cover?".

Connected through hyperedge HE-1 (induced by source paper "approximation algorithms for distributionally robust stochastic optimization with black box distributions"):
  with members:
    - "online allocation with traffic spikes mixing adversarial and stochastic models"
    - "making the most of your samples"
    - "robust and maxmin optimization under matroid and knapsack uncertainty sets"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-2 (induced by source paper "back to the source an online approach for sensor placement and source localization"):
  with members:
    - "observer placement for source localization the effect of budgets and transmission variance"
    - "back to the past source identification in diffusion networks from partially observed cascades"
    - "identification of source of rumors in social networks with incomplete information"
    - "approximability of the minimum weighted doubly resolving set problem"
    - "how to identify an infection source with limited observations"
    - "a robust information source estimator with sparse observations"
    - "rooting out the rumor culprit from suspects"
    - "information source detection in the sir model a sample path based approach"

Connected through hyperedge HE-3 (induced by source paper "constrained submodular maximization via greedy local search"):
  with members:
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "robust and maxmin optimization under matroid and knapsack uncertainty sets"

Connected through hyperedge HE-4 (induced by source paper "submodular function maximization via the multilinear relaxation and contention resolution schemes"):
  with members:
    - "symmetry and approximability of submodular maximization problems"
    - "maximizing non monotone submodular set functions subject to different constraints combined algorithms"
    - "geometric packing under non uniform constraints"
    - "mechanism design via correlation gap"
    - "robust and maxmin optimization under matroid and knapsack uncertainty sets"
    - "a note on concentration of submodular functions"
    - "submodular maximization by simulated annealing"
    - "correlation robust stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "constrained non monotone submodular maximization offline and secretary algorithms"):
  with members:
    - "symmetry and approximability of submodular maximization problems"
    - "correlation robust stochastic optimization"
    - "non monotone submodular maximization under matroid and knapsack constraints"
    - "algorithms for secretary problems on graphs and hypergraphs"

Connected through hyperedge HE-6 (induced by source paper "robust and maxmin optimization under matroid and knapsack uncertainty sets"):
  with members:
    - "constrained non monotone submodular maximization offline and secretary algorithms"
    - "is submodularity testable"
    - "correlation robust stochastic optimization"

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)