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: "bias aware sketches"
  Abstract: Count-Sketch \cite{CCFC02} and Count-Median \cite{CM04} are two widely used sketching algorithms for processing large-scale distributed and streaming datasets, such as finding frequent elements, computing frequency moments, performing point queries, etc. % Their popularity is largely due to the fact that linear sketches can be naturally composed in the distributed model and be efficiently updated in the streaming model. Moreover, both the sketching phase and the recovery phase can be parallelized. The errors of Count-Sketch and Count-Median are expressed in terms of the sum of coordinates of the input vector excluding those largest ones, or, the mass on the tail of the vector. Thus, the precondition for these algorithms to perform well is that the mass on the tail is small, which is, however, not always the case -- in many real-world datasets the coordinates of the input vector have a non-zero bias, which will generate a large mass on the tail. #R##N#In this paper we propose linear sketches that are {\em bias-aware}. They can be used as substitutes to Count-Sketch and Count-Median, and achieve strictly better error guarantees. We also demonstrate their practicality by an extensive experimental evaluation on both real and synthetic datasets.

Connected through hyperedge HE-1 (induced by source paper "count min optimal estimation and tight error bounds using empirical error distributions"):
  with members:
    - "count min log sketch approximately counting with approximate counters"
    - "improved concentration bounds for count sketch"
    - "what you can do with coordinated samples"

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)