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: "pareto optimization for subset selection with dynamic cost constraints"
  Abstract: In this paper, we consider the subset selection problem for function $f$ with constraint bound $B$ which changes over time. We point out that adaptive variants of greedy approaches commonly used in the area of submodular optimization are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach, we show that this algorithm efficiently computes a $\phi= (\alpha_f/2)(1-\frac{1}{e^{\alpha_f}})$-approximation, where $\alpha_f$ is the submodularity ratio of $f$, for each possible constraint bound $b \leq B$. Furthermore, we show that POMC is able to adapt its set of solutions quickly in the case that $B$ increases. Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms.

Connected through hyperedge HE-1 (induced by source paper "evolutionary bi objective optimization for the dynamic chance constrained knapsack problem based on tail bound objectives"):
  with members:
    - "evolutionary algorithms for the chance constrained knapsack problem"
    - "optimization of chance constrained submodular functions"

Connected through hyperedge HE-2 (induced by source paper "non monotone submodular maximization with multiple knapsacks in static and dynamic settings"):
  with members:
    - "optimization of chance constrained submodular functions"
    - "greedy maximization of functions with bounded curvature under partition matroid constraints"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "max sum diversification monotone submodular functions and dynamic updates"
    - "maximizing non monotone submodular set functions subject to different constraints combined algorithms"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-3 (induced by source paper "optimization of chance constrained submodular functions"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "evolutionary algorithms for the chance constrained knapsack problem"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "greedy maximization of functions with bounded curvature under partition matroid constraints"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "social dynamics of digg"

Connected through hyperedge HE-4 (induced by source paper "multi objective evolutionary algorithms are still good maximizing monotone approximately submodular minus modular functions"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "greedy maximization of functions with bounded curvature under partition matroid constraints"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "lazier than lazy greedy"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)