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: "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.

Connected through hyperedge HE-1 (induced by source paper "submodular matroid secretary problem with shortlists"):
  with members:
    - "quasi polynomial algorithms for submodular tree orienteering and other directed network design problems"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "submodular secretary problem with shortlists"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "a logarithmic integrality gap bound for directed steiner tree in quasi bipartite graphs"
    - "streaming algorithms for submodular function maximization"
    - "the submodular secretary problem goes linear"

Connected through hyperedge HE-2 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online"):
  with members:
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "on budget feasible mechanism design for symmetric submodular objectives"
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "budget feasible mechanisms on matroids"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "bayesian budget feasibility with posted pricing"

Connected through hyperedge HE-3 (induced by source paper "an improved algorithm for the submodular secretary problem with a cardinality constraint"):
  with members:
    - "combinatorial prophet inequalities"
    - "constrained submodular maximization via a non symmetric technique"
    - "building a good team secretary problems and the supermodular degree"
    - "the submodular secretary problem goes linear"
    - "the simulated greedy algorithm for several submodular matroid secretary problems"
    - "constrained non monotone submodular maximization offline and secretary algorithms"

Connected through hyperedge HE-4 (induced by source paper "sequential no substitution k median clustering"):
  with members:
    - "new frameworks for offline and streaming coreset constructions"
    - "an algorithm for online k means clustering"

Connected through hyperedge HE-5 (induced by source paper "submodular secretary problem with shortlists"):
  with members:
    - "online submodular maximization beating 1 2 made simple"
    - "the submodular secretary problem under a cardinality constraint and with limited resources"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "the submodular secretary problem goes linear"
    - "lazier than lazy greedy"
    - "submodular maximization meets streaming matchings matroids and more"
    - "online submodular welfare maximization greedy is optimal"
    - "online stochastic packing applied to display ad allocation"

Connected through hyperedge HE-6 (induced by source paper "packing returning secretaries"):
  with members:
    - "combinatorial secretary problems with ordinal information"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "the temp secretary problem"
    - "the submodular secretary problem goes linear"
    - "o log log rank competitive ratio for the matroid secretary problem"
    - "online independent set beyond the worst case secretaries prophets and periods"
    - "algorithms for secretary problems on graphs and hypergraphs"

Connected through hyperedge HE-7 (induced by source paper "selecting with history"):
  with members:
    - "the submodular secretary problem goes linear"
    - "a simple o log log mathrm rank competitive algorithm for the matroid secretary problem"

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)