human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "exponential separations between turnstile streaming and linear sketching"):
  with members:
    - "the sketching complexity of graph and hypergraph counting"
    - "optimality of linear sketching under modular updates"
    - "on estimating maximum matching size in graph streams"
    - "a hybrid sampling scheme for triangle counting"
    - "linear sketching over mathbb f_2"
    - "maximum matching in turnstile streams"

Question: Please tell me which class the center node 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)