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 "tight bounds for distributed functional monitoring"):
  Abstract: We resolve several fundamental questions in the area of distributed functional monitoring, initiated by Cormode, Muthukrishnan, and Yi (SODA, 2008). In this model there are $k$ sites each tracking their input and communicating with a central coordinator that continuously maintain an approximate output to a function $f$ computed over the union of the inputs. The goal is to minimize the communication. #R##N#We show the randomized communication complexity of estimating the number of distinct elements up to a $1+\eps$ factor is $\tilde{\Omega}(k/\eps^2)$, improving the previous $\Omega(k + 1/\eps^2)$ bound and matching known upper bounds up to a logarithmic factor. For the $p$-th frequency moment $F_p$, $p > 1$, we improve the previous $\Omega(k + 1/\eps^2)$ communication bound to $\tilde{\Omega}(k^{p-1}/\eps^2)$. We obtain similar improvements for heavy hitters, empirical entropy, and other problems. We also show that we can estimate $F_p$, for any $p > 1$, using $\tilde{O}(k^{p-1}\poly(\eps^{-1}))$ communication. This greatly improves upon the previous $\tilde{O}(k^{2p+1}N^{1-2/p} \poly(\eps^{-1}))$ bound of Cormode, Muthukrishnan, and Yi for general $p$, and their $\tilde{O}(k^2/\eps + k^{1.5}/\eps^3)$ bound for $p = 2$. For $p = 2$, our bound resolves their main open question. #R##N#Our lower bounds are based on new direct sum theorems for approximate majority, and yield significant improvements to problems in the data stream model, improving the bound for estimating $F_p, p > 2,$ in $t$ passes from $\tilde{\Omega}(n^{1-2/p}/(\eps^{2/p} t))$ to $\tilde{\Omega}(n^{1-2/p}/(\eps^{4/p} t))$, giving the first bound for estimating $F_0$ in $t$ passes of $\Omega(1/(\eps^2 t))$ bits of space that does not use the gap-hamming problem.

Includes members:
  Member "optimal random sampling from distributed streams revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication efficient weighted reservoir sampling from fully distributed data streams")
  Member "a lower bound for estimating high moments of a data stream":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on approximating functions of the singular values in a stream")
  Member "information complexity versus corruption and applications to orthogonality and gap hamming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "r enyi information complexity and an information theoretic characterization of the partition bound")
  Member "randomized algorithms for tracking distributed count frequencies and ranks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "lower bounds for number in hand multiparty communication complexity made easy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "polynomial estimators for high frequency moments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating large frequency moments with o n 1 2 k bits")
  Member "information cost tradeoffs for augmented index and streaming language recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "fast moment estimation in data streams in optimal space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "solving tall dense linear programs in nearly linear time")

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)