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: "nearly optimal distinct elements and heavy hitters on sliding windows"
  Abstract: We study the distinct elements and $\ell_p$-heavy hitters problems in the sliding window model, where only the most recent $n$ elements in the data stream form the underlying set. We first introduce the composable histogram, a simple twist on the exponential (Datar et al., SODA 2002) and smooth histograms (Braverman and Ostrovsky, FOCS 2007) that may be of independent interest. We then show that the composable histogram along with a careful combination of existing techniques to track either the identity or frequency of a few specific items suffices to obtain algorithms for both distinct elements and $\ell_p$-heavy hitters that are nearly optimal in both $n$ and $\epsilon$. #R##N#Applying our new composable histogram framework, we provide an algorithm that outputs a $(1+\epsilon)$-approximation to the number of distinct elements in the sliding window model and uses $\O{\frac{1}{\epsilon^2}\log n\log\frac{1}{\epsilon}\log\log n+\frac{1}{\epsilon}\log^2 n}$ bits of space. For $\ell_p$-heavy hitters, we provide an algorithm using space $\mathcal{O}\left(\frac{1}{\epsilon^p}\log^2 n\left(\log\log n+\log\frac{1}{\epsilon}\right)\right)$ for $0<p\le 2$, improving upon the best-known algorithm for $\ell_2$-heavy hitters (Braverman et al., COCOON 2014), which has space complexity $\mathcal{O}\left(\frac{1}{\epsilon^4}\log^3 n\right)$. We also show complementing nearly optimal lower bounds of $\Omega\left(\frac{1}{\epsilon}\log^2 n+\frac{1}{\epsilon^2}\log n\right)$ for distinct elements and $\Omega\left(\frac{1}{\epsilon^p}\log^2 n\right)$ for $\ell_p$-heavy hitters, both tight up to $\mathcal{O}(\log\log n)$ and $\mathcal{O}\left(\log\frac{1}{\epsilon}\right)$ factors.

Connected through hyperedge HE-1 (induced by source paper "improved algorithms for time decay streams"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "numerical linear algebra in the sliding window model"
    - "coresets for kernel regression"
    - "online facility location on semi random streams"
    - "new frameworks for offline and streaming coreset constructions"
    - "randomized composable core sets for distributed submodular maximization"
    - "frequent directions simple and deterministic matrix sketching"
    - "a new framework for distributed submodular maximization"

Connected through hyperedge HE-2 (induced by source paper "numerical linear algebra in the sliding window model"):
  with members:
    - "lectures on randomized numerical linear algebra"
    - "submodular optimization over sliding windows"
    - "sketches for matrix norms faster smaller and more general"
    - "submodular maximization over sliding windows"
    - "online row sampling"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "frequent directions simple and deterministic matrix sketching"
    - "sparsified cholesky solvers for sdd linear systems"

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)