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 "submodular secretary problems cardinality matching and linear constraints"):
  Abstract: We study various generalizations of the secretary problem with submodular objective functions. Generally, a set of requests is revealed step-by-step to an algorithm in random order. For each request, one option has to be selected so as to maximize a monotone submodular function while ensuring feasibility. For our results, we assume that we are given an offline algorithm computing an $\alpha$-approximation for the respective problem. This way, we separate computational limitations from the ones due to the online nature. When only focusing on the online aspect, we can assume $\alpha = 1$. #R##N#In the submodular secretary problem, feasibility constraints are cardinality constraints. That is, out of a randomly ordered stream of entities, one has to select a subset size $k$. For this problem, we present a $0.31\alpha$-competitive algorithm for all $k$, which asymptotically reaches competitive ratio $\frac{\alpha}{e}$ for large $k$. In submodular secretary matching, one side of a bipartite graph is revealed online. Upon arrival, each node has to be matched permanently to an offline node or discarded irrevocably. We give an $\frac{\alpha}{4}$-competitive algorithm. In both cases, we improve over previously best known competitive ratios, using a generalization of the algorithm for the classic secretary problem. #R##N#Furthermore, we give an $O(\alpha d^{-\frac{2}{B-1}})$-competitive algorithm for submodular function maximization subject to linear packing constraints. Here, $d$ is the column sparsity, that is the maximal number of none-zero entries in a column of the constraint matrix, and $B$ is the minimal capacity of the constraints. Notably, this bound is independent of the total number of constraints. We improve the algorithm to be $O(\alpha d^{-\frac{1}{B-1}})$-competitive if both $d$ and $B$ are known to the algorithm beforehand.

Includes members:
  Member "building a good team secretary problems and the supermodular degree":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond submodular maximization")
  Member "fast algorithms for online stochastic convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simple and fast algorithm for binary integer and online linear programming")
  Member "o log log rank competitive ratio for the matroid secretary problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "submodular matroid secretary problem with shortlists")
  Member "a simple o log log mathrm rank competitive algorithm for the matroid secretary problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "submodular matroid secretary problem with shortlists")
  Member "the simulated greedy algorithm for several submodular matroid secretary problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an improved algorithm for the submodular secretary problem with a cardinality constraint")
  Member "a dynamic near optimal algorithm for online linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random order models")
  Member "algorithms for secretary problems on graphs and hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "random order models")

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)