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: "a very efficient scheme for estimating entropy of data streams using compressed counting"
  Abstract: Compressed Counting (CC)} was recently proposed for approximating the $\alpha$th frequency moments of data streams, for $0<\alpha \leq 2$. Under the relaxed strict-Turnstile model, CC dramatically improves the standard algorithm based on symmetric stable random projections}, especially as $\alpha\to 1$. A direct application of CC is to estimate the entropy, which is an important summary statistic in Web/network measurement and often serves a crucial "feature" for data mining. The R\'enyi entropy and the Tsallis entropy are functions of the $\alpha$th frequency moments; and both approach the Shannon entropy as $\alpha\to 1$. A recent theoretical work suggested using the $\alpha$th frequency moment to approximate the Shannon entropy with $\alpha=1+\delta$ and very small $|\delta|$ (e.g., $<10^{-4}$). #R##N#In this study, we experiment using CC to estimate frequency moments, R\'enyi entropy, Tsallis entropy, and Shannon entropy, on real Web crawl data. We demonstrate the variance-bias trade-off in estimating Shannon entropy and provide practical recommendations. In particular, our experiments enable us to draw some important conclusions: #R##N#(1) As $\alpha\to 1$, CC dramatically improves {\em symmetric stable random projections} in estimating frequency moments, R\'enyi entropy, Tsallis entropy, and Shannon entropy. The improvements appear to approach "infinity." #R##N#(2) Using {\em symmetric stable random projections} and $\alpha = 1+\delta$ with very small $|\delta|$ does not provide a practical algorithm because the required sample size is enormous.

Connected through hyperedge HE-1 (induced by source paper "the optimal quantile estimator for compressed counting"):
  with members:
    - "sketching and streaming entropy via approximation theory"
    - "on approximating frequency moments of data streams with skewed projections"

Connected through hyperedge HE-2 (induced by source paper "computationally efficient estimators for dimension reductions using stable random projections"):
  with members:
    - "sketching and streaming entropy via approximation theory"

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)