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: "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 $m$.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for generating provably near optimal cluster descriptors for explainability"):
  with members:
    - "community detection in networks with node attributes"

Connected through hyperedge HE-2 (induced by source paper "group fairness in influence maximization"):
  with members:
    - "fair allocation of indivisible public goods"
    - "groupwise maximin fair allocation of indivisible goods"
    - "diverse weighted bipartite b matching"
    - "approximate maximin shares for groups of agents"
    - "multiwinner elections with diversity constraints"
    - "stochastic submodular maximization the case of coverage functions"
    - "gradient methods for submodular maximization"
    - "democratic fair allocation of indivisible goods"

Connected through hyperedge HE-3 (induced by source paper "running time analysis of the 1 1 ea for robust linear optimization"):
  with members:
    - "robust monotone submodular function maximization"
    - "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"

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)