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: "a lower bound for estimating high moments of a data stream"
  Abstract: We show an improved lower bound for the Fp estimation problem in a data stream setting for p>2. A data stream is a sequence of items from the domain [n] with possible repetitions. The frequency vector x is an n-dimensional non-negative integer vector x such that x(i) is the number of occurrences of i in the sequence. Given an accuracy parameter Omega(n^{-1/p}) < \epsilon < 1, the problem of estimating Fp is to estimate \norm{x}_p^p = \sum_{i \in [n]} \abs{x(i)}^p correctly to within a relative accuracy of 1\pm \epsilon with high constant probability in an online fashion and using as little space as possible. The current space lower bound for this problem is Omega(n^{1-2/p} \epsilon^{-2/p}+ n^{1-2/p}\epsilon^{-4/p}/ \log^{O(1)}(n)+ (\epsilon^{-2} + \log (n))). The first term in the lower bound expression was proved in \cite{B-YJKS:stoc02,cks:ccc03}, the second in \cite{wz:arxiv11} and the third in \cite{wood:soda04}. In this note, we show an Omega(p^2 n^{1-2/p} \epsilon^{-2}/\log (n)) bits space bound, for Omega(pn^{-1/p}) \le \epsilon \le 1/10.

Connected through hyperedge HE-1 (induced by source paper "on approximating functions of the singular values in a stream"):
  with members:
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "spectrum estimation from samples"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "sketching as a tool for numerical linear algebra"
    - "sketching and embedding are equivalent for norms"
    - "streaming sums in sublinear space"

Connected through hyperedge HE-2 (induced by source paper "taylor polynomial estimator for estimating frequency moments"):
  with members:
    - "approximating large frequency moments with o n 1 2 k bits"
    - "tight lower bound for linear sketches of moments"
    - "tight bounds for distributed functional monitoring"
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"
    - "online learning of noisy data with kernels"
    - "streaming algorithms from precision sampling"
    - "recursive sketching for frequency moments"

Connected through hyperedge HE-3 (induced by source paper "approximating large frequency moments with o n 1 2 k bits"):
  with members:
    - "polynomial estimators for high frequency moments"
    - "tight bounds for distributed functional monitoring"
    - "streaming algorithms from precision sampling"
    - "recursive sketching for frequency moments"
    - "asymptotically optimal lower bounds on the nih multi party information"

Connected through hyperedge HE-4 (induced by source paper "asymptotically exact streaming algorithms"):
  with members:
    - "asymptotically optimal algorithms for one to one pickup and delivery problems with applications to transportation systems"
    - "lower bounds for oblivious subspace embeddings"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-5 (induced by source paper "approximating large frequency moments with pick and drop sampling"):
  with members:
    - "how to catch l 2 heavy hitters on sliding windows"
    - "tight lower bound for linear sketches of moments"
    - "polynomial estimators for high frequency moments"
    - "tight bounds for distributed functional monitoring"
    - "recursive sketching for frequency moments"

Connected through hyperedge HE-6 (induced by source paper "tight bounds for distributed functional monitoring"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "information complexity versus corruption and applications to orthogonality and gap hamming"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "lower bounds for number in hand multiparty communication complexity made easy"
    - "polynomial estimators for high frequency moments"
    - "information cost tradeoffs for augmented index and streaming language recognition"
    - "fast moment estimation in data streams in optimal space"
    - "an optimal lower bound on the communication complexity of gap hamming distance"

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)