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 "multi objective weighted sampling"):
  Abstract: {\em Multi-objective samples} are powerful and versatile summaries of large data sets. For a set of keys $x\in X$ and associated values $f_x \geq 0$, a weighted sample taken with respect to $f$ allows us to approximate {\em segment-sum statistics} $\text{Sum}(f;H) = \text{sum}_{x\in H} f_x$, for any subset $H$ of the keys, with statistically-guaranteed quality that depends on sample size and the relative weight of $H$. When estimating $\text{Sum}(g;H)$ for $g\not=f$, however, quality guarantees are lost. A multi-objective sample with respect to a set of functions $F$ provides for each $f\in F$ the same statistical guarantees as a dedicated weighted sample while minimizing the summary size. #R##N#We analyze properties of multi-objective samples and present sampling schemes and meta-algortithms for estimation and optimization while showcasing two important application domains. The first are key-value data sets, where different functions $f\in F$ applied to the values correspond to different statistics such as moments, thresholds, capping, and sum. A multi-objective sample allows us to approximate all statistics in $F$. The second is metric spaces, where keys are points, and each $f\in F$ is defined by a set of points $C$ with $f_x$ being the service cost of $x$ by $C$, and $\text{Sum}(f;X)$ models centrality or clustering cost of $C$. A multi-objective sample allows us to estimate costs for each $f\in F$. In these domains, multi-objective samples are often of small size, are efficiently to construct, and enable scalable estimation and optimization. We aim here to facilitate further applications of this powerful technique.

Includes members:
  Member "stream sampling for frequency cap statistics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data streams with bounded deletions")
  Member "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable distributed approximation of internal measures for clustering evaluation")
  Member "all distances sketches revisited hip estimators for massive graphs analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sampling sketches for concave sublinear functions of frequencies")
  Member "hyperanf approximating the neighbourhood function of very large graphs on a budget":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "coordinated weighted sampling for estimating aggregates over multiple weight assignments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self similar epochs value in arrangement")
  Member "stream sampling for variance optimal estimation of subset sums":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the adversarial robustness of sampling")

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.DB(Databases)