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 submodular maximization offline and online algorithms"
  Abstract: In this work, we consider the robust submodular maximization problem subject a matroid constraint in the offline and online setting. In the offline version, we are given a collection of $k$ monotone submodular functions and matroid on a ground set of size $n$. The goal is to select one independent set that maximizes the minimum of the submodular functions. Given the complexity of the problem, we design a bi-criteria approximation algorithm that returns a set that is the union of a logarithmic number of independent sets. In the online version of the problem, we receive a new collection of functions at each time step and aim to pick an independent set in every stage. We measure the performance of the algorithm in the regret setting. Again, we give a bi-criteria approximation algorithm which gives a (nearly) optimal approximation as well as regret bounds. This result rely crucially on modifying the Follow-the-Perturbed-Leader algorithm of [Kalai and Vempala, 2005].

Connected through hyperedge HE-1 (induced by source paper "robust submodular minimization with applications to cooperative modeling"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "approximating some network problems with scenarios"
    - "robust optimization for non convex objectives"
    - "equilibrium computation and robust optimization in zero sum games with submodular structure"
    - "polyhedral aspects of submodularity convexity and concavity"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "fast semidifferential based submodular function optimization"

Connected through hyperedge HE-2 (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"
    - "robust monotone submodular function maximization"
    - "distributionally robust submodular maximization"
    - "approximating some network problems with scenarios"
    - "a framework towards domain specific video summarization"
    - "robust optimization for non convex objectives"
    - "equilibrium computation and robust optimization in zero sum games with submodular structure"

Connected through hyperedge HE-3 (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"
    - "robust monotone submodular function maximization"
    - "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"

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 monotone submodular function maximization"
    - "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 "distributionally robust submodular maximization"):
  with members:
    - "robust monotone submodular function maximization"
    - "robust optimization for non convex objectives"
    - "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-6 (induced by source paper "efficient algorithms for robust submodular maximization under matroid constraints"):
  with members:
    - "robust budget allocation via continuous submodular functions"
    - "lazier than lazy greedy"

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)