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 "multi objective maximization of monotone submodular functions with cardinality constraint"):
  Abstract: We consider the problem of multi-objective maximization of monotone submodular functions subject to cardinality constraint, one formulation of which is $\max_{|A|=k}\min_{i\in\{1,\dots,m\}}f_i(A)$. Krause et al. (2008) showed that when the number of functions $m$ grows as the cardinality $k$ i.e., $m=\Omega(k)$, the problem is inapproximable (unless $P=NP$). For the more general case of matroid constraint, Chekuri et al. (2010) gave a randomized $(1-1/e)-\epsilon$ approximation for constant $m$. The runtime (number of queries to function oracle) scales exponentially as $n^{m/\epsilon^3}$. We give the first polynomial time asymptotically constant factor approximations for $m=o(\frac{k}{\log^3 k})$: $(i)$ A randomized $(1-1/e)$ algorithm based on Chekuri et al. (2010). $(ii)$ A faster and more practical $\tilde{O}(n/\delta^3)$ time, randomized $(1-1/e)^2-\delta$ approximation based on Multiplicative-Weight-Updates. Finally, we characterize the variation in optimal solution value as a function of the cardinality $k$, leading to a derandomized approximation for constant $m$.

Includes members:
  Member "robust monotone submodular function maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified framework of robust submodular optimization")
  Member "robust optimization for non convex objectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distribution agnostic model agnostic meta learning")
  Member "lazier than lazy greedy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "near optimally teaching the crowd to classify":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "streaming submodular maximization under a k set system constraint")
  Member "near optimal nonmyopic value of information in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "randomized rounding without solving the linear program":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "sequential and parallel algorithms for mixed packing and covering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed assignment with limited communication for multi robot multi target tracking")

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)