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 "robust monotone submodular function maximization"):
  Abstract: We consider a robust formulation, introduced by Krause et al. (J Artif Intell Res 42:427–486, 2011), of the classical cardinality constrained monotone submodular function maximization problem, and give the first constant factor approximation results. The robustness considered is w.r.t. adversarial removal of up to #N#                  #N#                    #N#                  #N#                  $$\tau $$#N#                  #N#                    #N#                  #N#                 elements from the chosen set. For the fundamental case of #N#                  #N#                    #N#                  #N#                  $$\tau =1$$#N#                  #N#                    #N#                  #N#                , we give a deterministic #N#                  #N#                    #N#                  #N#                  $$(1-1/e)-1/\varTheta (m)$$#N#                  #N#                    #N#                  #N#                 approximation algorithm, where m is an input parameter and number of queries scale as #N#                  #N#                    #N#                  #N#                  $$O(n^{m+1})$$#N#                  #N#                    #N#                  #N#                . In the process, we develop a deterministic #N#                  #N#                    #N#                  #N#                  $$(1-1/e)-1/\varTheta (m)$$#N#                  #N#                    #N#                  #N#                 approximate greedy algorithm for bi-objective maximization of (two) monotone submodular functions. Generalizing the ideas and using a result from Chekuri et al. (in: FOCS 10, IEEE, pp 575–584, 2010), we show a randomized #N#                  #N#                    #N#                  #N#                  $$(1-1/e)-\epsilon $$#N#                  #N#                    #N#                  #N#                 approximation for constant #N#                  #N#                    #N#                  #N#                  $$\tau $$#N#                  #N#                    #N#                  #N#                 and #N#                  #N#                    #N#                  #N#                  $$\epsilon \le \frac{1}{\tilde{\varOmega }(\tau )}$$#N#                  #N#                    #N#                  #N#                , making #N#                  #N#                    #N#                  #N#                  $$O(n^{1/\epsilon ^3})$$#N#                  #N#                    #N#                  #N#                 queries. Further, for #N#                  #N#                    #N#                  #N#                  $$\tau \ll \sqrt{k}$$#N#                  #N#                    #N#                  #N#                , we give a fast and practical 0.387 algorithm. Finally, we also give a black box result result for the much more general setting of robust maximization subject to an Independence System.

Includes members:
  Member "deterministic algorithms for submodular maximization problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness")
  Member "symmetry and approximability of submodular maximization problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "submodular function maximization via the multilinear relaxation and contention resolution schemes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "adaptive submodularity theory and applications in active learning and stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comprehensive and efficient data labeling via adaptive model scheduling")
  Member "submodular maximization by simulated annealing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")

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)