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: "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.

Connected through hyperedge HE-1 (induced by source paper "a unified framework of robust submodular optimization"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "distributionally robust submodular maximization"
    - "approximating some network problems with scenarios"
    - "a framework towards domain specific video summarization"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"
    - "equilibrium computation and robust optimization in zero sum games with submodular structure"

Connected through hyperedge HE-2 (induced by source paper "adversarially robust submodular maximization under knapsack constraints"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "distributionally robust submodular maximization"
    - "submodular secretary problem with shortlists"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-3 (induced by source paper "distributed attack robust submodular maximization for multi robot planning"):
  with members:
    - "the impact of information in distributed submodular maximization"
    - "distributed state estimation using intermittently connected robot networks"
    - "resilient active information gathering with mobile robots"
    - "resilient active target tracking with multiple robots"
    - "distributed submodular maximization with limited information"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "fast algorithms for the maximum clique problem on massive sparse graphs"
    - "high speed flight in an ergodic forest"

Connected through hyperedge HE-4 (induced by source paper "adaptive robust optimization with nearly submodular structure"):
  with members:
    - "near optimal adaptivity gaps for stochastic multi value probing"
    - "robust submodular maximization offline and online algorithms"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "robust influence maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "running time analysis of the 1 1 ea for robust linear optimization"):
  with members:
    - "multi objective maximization of monotone submodular functions with cardinality constraint"
    - "robust influence maximization"
    - "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"
    - "analyzing evolutionary optimization in noisy environments"
    - "multiplicative drift analysis"

Connected through hyperedge HE-6 (induced by source paper "distributionally robust submodular maximization"):
  with members:
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"
    - "stochastic submodular maximization the case of coverage functions"
    - "equilibrium computation and robust optimization in zero sum games with submodular structure"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "resilient active information gathering with mobile robots"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "information acquisition with sensing robots algorithms and error bounds"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "high speed flight in an ergodic forest"

Connected through hyperedge HE-8 (induced by source paper "randomized strategies for robust combinatorial optimization"):
  with members:
    - "robust optimization for non convex objectives"
    - "robust randomized matchings"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

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)