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 "streaming algorithms for bin packing and vector scheduling"):
  Abstract: Problems involving the efficient arrangement of simple objects, as captured by bin packing and makespan scheduling, are fundamental tasks in combinatorial optimization. These are well understood in the traditional online and offline cases, but have been less well-studied when the volume of the input is truly massive, and cannot even be read into memory. This is captured by the streaming model of computation, where the aim is to approximate the cost of the solution in one pass over the data, using small space. As a result, streaming algorithms produce concise input summaries that approximately preserve the optimum value. #R##N#We design the first efficient streaming algorithms for these fundamental problems in combinatorial optimization. For Bin Packing, we provide a streaming asymptotic $1+\varepsilon$-approximation with $\widetilde{O}\left(\frac{1}{\varepsilon}\right)$ memory, where $\widetilde{O}$ hides logarithmic factors. Moreover, such a space bound is essentially optimal. Our algorithm implies a streaming $d+\varepsilon$-approximation for Vector Bin Packing in $d$ dimensions, running in space $\widetilde{O}\left(\frac{d}{\varepsilon}\right)$. For the related Vector Scheduling problem, we show how to construct an input summary in space $\widetilde{O}(d^2\cdot m / \varepsilon^2)$ that preserves the optimum value up to a factor of $2 - \frac{1}{m} +\varepsilon$, where $m$ is the number of identical machines.

Includes members:
  Member "tight lower bound for comparison based quantile summaries":
    also connected through other hyperedges:
      (none)
  Member "a new and improved algorithm for online bin packing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online bin covering with advice")
  Member "fully dynamic bin packing with limited repacking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "permutation strikes back the power of recourse in online metric matching")
  Member "optimal quantile approximation in streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "storyboard optimizing precomputed summaries for aggregation")
  Member "closing the gap for makespan scheduling via sparsification techniques":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an algorithmic framework for approximating maximin share allocation of chores")
  Member "a logarithmic additive integrality gap for bin packing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online bin covering with advice")
  Member "tight bounds for online vector scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "draps dynamic and resource aware placement scheme for docker containers in a heterogeneous cluster")
  Member "polynomiality for bin packing with a constant number of item types":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an optimal algorithm for 1 d cutting stock problem")

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)