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: "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
  Abstract: Many data sources are naturally modeled by multiple weight assignments over a set of keys: snapshots of an evolving database at multiple points in time, measurements collected over multiple time periods, requests for resources served at multiple locations, and records with multiple numeric attributes. Over such vector-weighted data we are interested in aggregates with respect to one set of weights, such as weighted sums, and aggregates over multiple sets of weights such as the $L_1$ difference. #R##N#Sample-based summarization is highly effective for data sets that are too large to be stored or manipulated. The summary facilitates approximate processing queries that may be specified after the summary was generated. #R##N#Current designs, however, are geared for data sets where a single {\em scalar} weight is associated with each key. #R##N#We develop a sampling framework based on {\em coordinated weighted samples} that is suited for multiple weight assignments and obtain estimators that are {\em orders of magnitude tighter} than previously possible. #R##N#We demonstrate the power of our methods through an extensive empirical evaluation on diverse data sets ranging from IP network to stock quotes data.

Connected through hyperedge HE-1 (induced by source paper "self similar epochs value in arrangement"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "distance queries from sampled data accurate and efficient"

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"
    - "multi objective weighted sampling"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "efficient and private approximations of distributed databases calculations"):
  with members:
    - "a minimal variance estimator for the cardinality of big data set intersection"
    - "parallel correctness and transferability for conjunctive queries"
    - "when distributed computation is communication expensive"
    - "calibrating data to sensitivity in private data analysis"
    - "what you can do with coordinated samples"
    - "explicit learning curves for transduction and application to clustering and compression algorithms"

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

Connected through hyperedge HE-5 (induced by source paper "multi objective weighted sampling"):
  with members:
    - "stream sampling for frequency cap statistics"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-6 (induced by source paper "stream sampling for frequency cap statistics"):
  with members:
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-7 (induced by source paper "timed influence computation and maximization"):
  with members:
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "axioms for centrality"
    - "in core computation of geometric centralities with hyperball a hundred billion nodes and beyond"
    - "trace complexity of network inference"
    - "scalable influence estimation in continuous time diffusion networks"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-8 (induced by source paper "variance competitiveness for monotone estimation tightening the bounds"):
  with members:
    - "distance queries from sampled data accurate and efficient"
    - "what you can do with coordinated samples"
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"

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)