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 "guarantees of stochastic greedy algorithms for non monotone submodular maximization with cardinality constraint"):
  Abstract: Submodular maximization with a cardinality constraint can model various problems, and those problems are often very large in practice. For the case where objective functions are monotone, many fast approximation algorithms have been developed. The stochastic greedy algorithm (SG) is one such algorithm, which is widely used thanks to its simplicity, efficiency, and high empirical performance. However, its approximation guarantee has been proved only for monotone objective functions. When it comes to non-monotone objective functions, existing approximation algorithms are inefficient relative to the fast algorithms developed for the case of monotone objectives. In this paper, we prove that SG (with slight modification) can achieve almost $1/4$-approximation guarantees in expectation in linear time even if objective functions are non-monotone. Our result provides a constant-factor approximation algorithm with the fewest oracle queries for non-monotone submodular maximization with a cardinality constraint. Experiments validate the performance of (modified) SG.

Includes members:
  Member "interlaced greedy algorithm for maximization of submodular functions in nearly linear time":
    also connected through other hyperedges:
      (none)
  Member "a memoization framework for scaling submodular optimization to large scale problems":
    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-1 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "submodular maximization beyond non negativity guarantees fast algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized submodular maximization at scale")
  Member "stochastic greedy closing the optimality gap in exact weak submodular maximization":
    also connected through other hyperedges:
      (none)
  Member "non monotone submodular maximization in exponentially fewer iterations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a polynomial lower bound on adaptive complexity of submodular maximization")
  Member "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scheduling observers over a shared channel with hard delivery deadlines")
  Member "pmlb a large benchmark suite for machine learning evaluation and comparison":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the tree ensemble layer differentiability meets conditional computation")

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)