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 "a polynomial lower bound on adaptive complexity of submodular maximization"):
  Abstract: In large-data applications, it is desirable to design algorithms with a high degree of parallelization. In the context of submodular optimization, adaptive complexity has become a widely-used measure of an algorithm's "sequentiality". Algorithms in the adaptive model proceed in rounds, and can issue polynomially many queries to a function $f$ in each round. The queries in each round must be independent, produced by a computation that depends only on query results obtained in previous rounds. #R##N#In this work, we examine two fundamental variants of submodular maximization in the adaptive complexity model: cardinality-constrained monotone maximization, and unconstrained non-monotone maximization. Our main result is that an $r$-round algorithm for cardinality-constrained monotone maximization cannot achieve a factor better than $1 - 1/e - \Omega(\min \{ \frac{1}{r}, \frac{\log^2 n}{r^3} \})$, for any $r  0$ is some constant). This is the first result showing that the number of rounds must blow up polynomially large as we approach the optimal factor of $1-1/e$. #R##N#For the unconstrained non-monotone maximization problem, we show a positive result: For every instance, and every $\delta>0$, either we obtain a $(1/2-\delta)$-approximation in $1$ round, or a $(1/2+\Omega(\delta^2))$-approximation in $O(1/\delta^2)$ rounds. In particular (in contrast to the cardinality-constrained case), there cannot be an instance where (i) it is impossible to achieve a factor better than $1/2$ regardless of the number of rounds, and (ii) it takes $r$ rounds to achieve a factor of $1/2-O(1/r)$.

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 "the fast algorithm for submodular maximization":
    also connected through other hyperedges:
      (none)
  Member "non monotone submodular maximization with nearly optimal adaptivity and query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "guarantees of stochastic greedy algorithms for non monotone submodular maximization with cardinality constraint")
  Member "an exponential speedup in parallel running time for submodular maximization without loss in approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "submodular function maximization in parallel via the multilinear relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "submodular maximization with optimal approximation adaptivity and query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "submodular optimization in the mapreduce model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simple and sharp analysis of k means")
  Member "unconstrained submodular maximization with constant adaptive complexity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regret bounds for batched bandits")

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)