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 problem with shortlists"
  Abstract: In submodular $k$-secretary problem, the goal is to select $k$ items in a randomly ordered input so as to maximize the expected value of a given monotone submodular function on the set of selected items. In this paper, we introduce a relaxation of this problem, which we refer to as submodular $k$-secretary problem with shortlists. In the proposed problem setting, the algorithm is allowed to choose more than $k$ items as part of a shortlist. Then, after seeing the entire input, the algorithm can choose a subset of size $k$ from the bigger set of items in the shortlist. We are interested in understanding to what extent this relaxation can improve the achievable competitive ratio for the submodular $k$-secretary problem. In particular, using an $O(k)$ shortlist, can an online algorithm achieve a competitive ratio close to the best achievable online approximation factor for this problem? We answer this question affirmatively by giving a polynomial time algorithm that achieves a $1-1/e-\epsilon -O(k^{-1})$ competitive ratio for any constant $\epsilon > 0$, using a shortlist of size $\eta_\epsilon(k) = O(k)$. Also, for the special case of m-submodular functions, we demonstrate an algorithm that achieves a $1-\epsilon$ competitive ratio for any constant $\epsilon > 0$, using an $O(1)$ shortlist. Finally, we show that our algorithm can be implemented in the streaming setting using a memory buffer of size $\eta_\epsilon(k) = O(k)$ to achieve a $1 - 1/e - \epsilon-O(k^{-1})$ approximation for submodular function maximization in the random order streaming model. This substantially improves upon the previously best known approximation factor of $1/2 + 8 \times 10^{-14}$ [Norouzi-Fard et al. 2018] that used a memory buffer of size $O(k \log k)$.

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"
    - "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"
    - "submodular secretary problems cardinality matching and linear constraints"
    - "streaming algorithms for submodular function maximization"
    - "the submodular secretary problem goes linear"

Connected through hyperedge HE-2 (induced by source paper "bring your own greedy max near optimal 1 2 approximations for submodular knapsack"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "multi pass streaming algorithms for monotone submodular function maximization"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "do less get more streaming submodular maximization with subsampling"

Connected through hyperedge HE-3 (induced by source paper "adversarially robust submodular maximization under knapsack constraints"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "robust monotone submodular function maximization"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "distributionally robust submodular maximization"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

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)