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: "distance queries from sampled data accurate and efficient"
  Abstract: Distance queries are a basic tool in data analysis. They are used for detection and localization of change for the purpose of anomaly detection, monitoring, or planning. Distance queries are particularly useful when data sets such as measurements, snapshots of a system, content, traffic matrices, and activity logs are collected repeatedly. #R##N#Random sampling, which can be efficiently performed over streamed or distributed data, is an important tool for scalable data analysis. The sample constitutes an extremely flexible summary, which naturally supports domain queries and scalable estimation of statistics, which can be specified after the sample is generated. The effectiveness of a sample as a summary, however, hinges on the estimators we have. #R##N#We derive novel estimators for estimating $L_p$ distance from sampled data. Our estimators apply with the most common weighted sampling schemes: Poisson Probability Proportional to Size (PPS) and its fixed sample size variants. They also apply when the samples of different data sets are independent or coordinated. Our estimators are admissible (Pareto optimal in terms of variance) and have compelling properties. #R##N#We study the performance of our Manhattan and Euclidean distance ($p=1,2$) estimators on diverse datasets, demonstrating scalability and accuracy even when a small fraction of the data is sampled. Our work, for the first time, facilitates effective distance estimation over sampled 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"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"

Connected through hyperedge HE-2 (induced by source paper "variance competitiveness for monotone estimation tightening the bounds"):
  with members:
    - "what you can do with coordinated samples"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "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.DS(Data Structures and Algorithms)