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: "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.

Connected through hyperedge HE-1 (induced by source paper "sample complexity bounds for influence maximization"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "the limitations of optimization from samples"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-2 (induced by source paper "clustering small samples with quality guarantees adaptivity with one2all pps"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a constant factor bi criteria approximation guarantee for k means"
    - "the hardness of approximation of euclidean k means"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "optimal time bounds for approximate clustering"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-3 (induced by source paper "semi supervised learning on graphs through reach and distance diffusion"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "scalable facility location for massive graphs on pregel like systems"
    - "reverse ranking by graph structure model and scalable algorithms"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "deepwalk online learning of social representations"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "distance based influence in networks computation and maximization"

Connected through hyperedge HE-4 (induced by source paper "estimating frequency statistics through distinct count measurements"):
  with members:
    - "stream sampling for frequency cap statistics"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "structure aware sampling flexible and accurate summarization"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-5 (induced by source paper "reverse ranking by graph structure model and scalable algorithms"):
  with members:
    - "scalable facility location for massive graphs on pregel like systems"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "timed influence computation and maximization"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "trace complexity of network inference"
    - "scalable influence estimation in continuous time diffusion networks"
    - "uncovering the temporal dynamics of diffusion networks"

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.DB(Databases)