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 "towards practical constrained monotone submodular maximization"):
  Abstract: We design new algorithms for maximizing a monotone non-negative submodular function under various constraints, which improve the state-of-the-art in time complexity and/or performance guarantee. We first investigate the cardinality constrained submodular maximization problem that has been widely studied for about four decades. We design an $(1-\frac{1}{e}-\varepsilon)$-approximation algorithm that makes $O(n\cdot \max \{\varepsilon^{-1},\log\log k \})$ queries. To the best of our knowledge, this is the fastest known algorithm. We further answer the open problem on finding a lower bound on the number of queries. We show that, no (randomized) algorithm can achieve a ratio better than $(\frac{1}{2}+\Theta(1))$ with $o(\frac{n}{\log n})$ queries. The acceleration above is achieved by our \emph{Adaptive Decreasing Threshold} (ADT) algorithm. Based on ADT, we study the $p$-system and $d$ knapsack constrained maximization problem. We show that an $(1/(p+\frac{7}{4}d+1)-\varepsilon)$-approximate solution can be computed via $O(\frac{n}{\varepsilon}\log \frac{n}{\varepsilon}\max\{\log \frac{1}{\varepsilon},\log\log n\})$ queries. Note that it improves the state of the art in both time complexity and approximation ratio. We also show how to improve the ratio for a single knapsack constraint via $O(n\cdot \max \{\varepsilon^{-1},\log\log k \})$ queries. For maximizing a submodular function with curvature $\kappa$ under matroid constraint, we show an $(1-\frac{\kappa}{e}-\varepsilon)$-approximate algorithm that uses $\tilde{O}(nk)$ value oracle queries. Our ADT could be utilized to obtain faster algorithms in other problems. To prove our results, we introduce a general characterization between randomized complexity and deterministic complexity of approximation algorithms that could be used in other problems and may be interesting in its own right.

Includes members:
  Member "lazier than lazy greedy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "optimal approximation for submodular and supermodular optimization with bounded curvature":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized submodular maximization at scale")
  Member "submodular maximization by simulated annealing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints")

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)