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 "variability in data streams"):
  Abstract: We consider the problem of tracking with small relative error an integer function $f(n)$ defined by a distributed update stream $f'(n)$. Existing streaming algorithms with worst-case guarantees for this problem assume $f(n)$ to be monotone; there are very large lower bounds on the space requirements for summarizing a distributed non-monotonic stream, often linear in the size $n$ of the stream. #R##N#Input streams that give rise to large space requirements are highly variable, making relatively large jumps from one timestep to the next. However, streams often vary slowly in practice. What has heretofore been lacking is a framework for non-monotonic streams that admits algorithms whose worst-case performance is as good as existing algorithms for monotone streams and degrades gracefully for non-monotonic streams as those streams vary more quickly. #R##N#In this paper we propose such a framework. We introduce a new stream parameter, the "variability" $v$, deriving its definition in a way that shows it to be a natural parameter to consider for non-monotonic streams. It is also a useful parameter. From a theoretical perspective, we can adapt existing algorithms for monotone streams to work for non-monotonic streams, with only minor modifications, in such a way that they reduce to the monotone case when the stream happens to be monotone, and in such a way that we can refine the worst-case communication bounds from $\Theta(n)$ to $\tilde{O}(v)$. From a practical perspective, we demonstrate that $v$ can be small in practice by proving that $v$ is $O(\log f(n))$ for monotone streams and $o(n)$ for streams that are "nearly" monotone or that are generated by random walks. We expect $v$ to be $o(n)$ for many other interesting input classes as well.

Includes members:
  Member "randomized algorithms for tracking distributed count frequencies and ranks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "tight bounds for distributed functional monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "optimal tracking of distributed heavy hitters and quantiles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the power of multiple anonymous messages")
  Member "cr precis a deterministic summary structure for update data streams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on low risk heavy hitters and sparse recovery schemes")

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)