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 "count min optimal estimation and tight error bounds using empirical error distributions"):
  Abstract: The Count-Min sketch is an important and well-studied data summarization method. It allows one to estimate the count of any item in a stream using a small, fixed size data sketch. However, the accuracy of the sketch depends on characteristics of the underlying data. This has led to a number of count estimation procedures which work well in one scenario but perform poorly in others. A practitioner is faced with two basic, unanswered questions. Which variant should be chosen when the data is unknown? Given an estimate, is its error sufficiently small to be trustworthy? #R##N#We provide answers to these questions. We derive new count estimators, including a provably optimal estimator, which best or match previous estimators in all scenarios. We also provide practical, tight error bounds at query time for both new and existing estimators. These error estimates also yield procedures to choose the sketch tuning parameters optimally, as they can extrapolate the error to different choices of sketch width and depth. #R##N#The key observation is that the distribution of errors in each counter can be empirically estimated from the sketch itself. By first estimating this distribution, count estimation becomes a statistical estimation and inference problem with a known error distribution. This provides both a principled way to derive new and optimal estimators as well as a way to study the error and properties of existing estimators.

Includes members:
  Member "bias aware sketches":
    also connected through other hyperedges:
      (none)
  Member "count min log sketch approximately counting with approximate counters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sf sketch slim fat sketch with gpu assistance")
  Member "improved concentration bounds for count sketch":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "what you can do with coordinated samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient and private approximations of distributed databases calculations")

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.DS(Data Structures and Algorithms)