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 "data streams with bounded deletions"):
  Abstract: Two prevalent models in the data stream literature are the insertion-only and turnstile models. Unfortunately, many important streaming problems require a $\Theta(\log(n))$ multiplicative factor more space for turnstile streams than for insertion-only streams. This complexity gap often arises because the underlying frequency vector $f$ is very close to $0$, after accounting for all insertions and deletions to items. Signal detection in such streams is difficult, given the large number of deletions. #R##N#In this work, we propose an intermediate model which, given a parameter $\alpha \geq 1$, lower bounds the norm $\|f\|_p$ by a $1/\alpha$-fraction of the $L_p$ mass of the stream had all updates been positive. Here, for a vector $f$, $\|f\|_p = \left (\sum_{i=1}^n |f_i|^p \right )^{1/p}$, and the value of $p$ we choose depends on the application. This gives a fluid medium between insertion only streams (with $\alpha = 1$), and turnstile streams (with $\alpha = \text{poly}(n)$), and allows for analysis in terms of $\alpha$. #R##N#We show that for streams with this $\alpha$-property, for many fundamental streaming problems we can replace a $O(\log(n))$ factor in the space usage for algorithms in the turnstile model with a $O(\log(\alpha))$ factor. This is true for identifying heavy hitters, inner product estimation, $L_0$ estimation, $L_1$ estimation, $L_1$ sampling, and support sampling. For each problem, we give matching or nearly matching lower bounds for $\alpha$-property streams. We note that in practice, many important turnstile data streams are in fact $\alpha$-property streams for small values of $\alpha$. For such applications, our results represent significant improvements in efficiency for all the aforementioned problems.

Includes members:
  Member "optimal lower bounds for universal relation and for samplers and finding duplicates in streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "the sparse awakens streaming algorithms for matching size estimation in sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "an optimal algorithm for l1 heavy hitters in insertion streams and related problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating entropy of distributions in constant space")
  Member "bptree an ell_2 heavy hitters algorithm using constant memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "beating countsketch for heavy hitters in insertion streams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rambo repeated and merged bloom filter for multiple set membership testing msmt in sub linear time")
  Member "maximum matching in turnstile streams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "stream sampling for frequency cap statistics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perfect l_p sampling in a data stream")
  Member "structure aware sampling flexible and accurate summarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "storyboard optimizing precomputed summaries for aggregation")

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)