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 "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model"):
  Abstract: Maximizing a monotone submodular function under various constraints is a classical and intensively studied problem. However, in the single-pass streaming model, where the elements arrive one by one and an algorithm can store only a small fraction of input elements, there is much gap in our knowledge, even though several approximation algorithms have been proposed in the literature. #R##N#In this work, we present the first lower bound on the approximation ratios for cardinality and matroid constraints that beat $1-\frac{1}{e}$ in the single-pass streaming model. Let $n$ be the number of elements in the stream. Then, we prove that any (randomized) streaming algorithm for a cardinality constraint with approximation ratio $\frac{2}{2+\sqrt{2}}+\varepsilon$ requires $\Omega\left(\frac{n}{K^2}\right)$ space for any $\varepsilon>0$, where $K$ is the size limit of the output set. We also prove that any (randomized) streaming algorithm for a (partition) matroid constraint with approximation ratio $\frac{K}{2K-1}+\varepsilon$ requires $\Omega\left(\frac{n}{K}\right)$ space for any $\varepsilon>0$, where $K$ is the rank of the given matroid. #R##N#In addition, we give streaming algorithms when we only have a weak oracle with which we can only evaluate function values on feasible sets. Specifically, we show weak-oracle streaming algorithms for cardinality and matroid constraints with approximation ratios $\frac{K}{2K-1}$ and $\frac{1}{2}$, respectively, whose space complexity is exponential in $K$ but is independent of $n$. The former one exactly matches the known inapproximability result for a cardinality constraint in the weak oracle model. #R##N#The latter one almost matches our lower bound of $\frac{K}{2K-1}$ for a matroid constraint, which almost settles the approximation ratio for a matroid constraint that can be obtained by a streaming algorithm whose space complexity is independent of $n$.

Includes members:
  Member "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized submodular maximization at scale")
  Member "an exponential speedup in parallel running time for submodular maximization without loss in approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "multi pass streaming algorithms for monotone submodular function maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bring your own greedy max near optimal 1 2 approximations for submodular knapsack")
  Member "towards nearly linear time algorithms for submodular maximization with a matroid constraint":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the fast algorithm for submodular maximization")
  Member "submodular function maximization in parallel via the multilinear relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "beyond 1 2 approximation for submodular maximization on massive data streams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularized submodular maximization at scale")
  Member "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "a nearly linear time algorithm for submodular maximization with a knapsack constraint":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")

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)