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: "measuring independence of datasets"
  Abstract: A data stream model represents setting where approximating pairwise, or $k$-wise, independence with sublinear memory is of considerable importance. In the streaming model the joint distribution is given by a stream of $k$-tuples, with the goal of testing correlations among the components measured over the entire stream. In the streaming model, Indyk and McGregor (SODA 08) recently gave exciting new results for measuring pairwise independence. The Indyk and McGregor methods provide $\log{n}$-approximation under statistical distance between the joint and product distributions in the streaming model. Indyk and McGregor leave, as their main open question, the problem of improving their $\log n$-approximation for the statistical distance metric. #R##N#In this paper we solve the main open problem posed by of Indyk and McGregor for the statistical distance for pairwise independence and extend this result to any constant $k$. In particular, we present an algorithm that computes an $(\epsilon, \delta)$-approximation of the statistical distance between the joint and product distributions defined by a stream of $k$-tuples. Our algorithm requires $O(({1\over \epsilon}\log({nm\over \delta}))^{(30+k)^k})$ memory and a single pass over the data stream.

Connected through hyperedge HE-1 (induced by source paper "continuous monitoring of ell_p norms in data streams"):
  with members:
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "beating countsketch for heavy hitters in insertion streams"
    - "streaming symmetric norms via measure concentration"
    - "taylor polynomial estimator for estimating frequency moments"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "tracking the frequency moments at all times"
    - "fast moment estimation in data streams in optimal space"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-2 (induced by source paper "finding subcube heavy hitters in analytics data streams"):
  with members:
    - "testing ising models"
    - "graphical model sketch"
    - "space lower bounds for itemset frequency sketches"
    - "learning to filter spam e mail a comparison of a naive bayesian and a memory based approach"

Connected through hyperedge HE-3 (induced by source paper "finding subcube heavy hitters in data streams"):
  with members:
    - "graphical model sketch"
    - "space lower bounds for itemset frequency sketches"

Connected through hyperedge HE-4 (induced by source paper "streaming algorithms from precision sampling"):
  with members:
    - "fast moment estimation in data streams in optimal space"
    - "recursive sketching for frequency moments"
    - "polylogarithmic approximation for edit distance and the asymmetric query complexity"
    - "stream sampling for variance optimal estimation of subset sums"

Connected through hyperedge HE-5 (induced by source paper "recursive sketching for frequency moments"):
  with members:
    - "fast moment estimation in data streams in optimal space"

Connected through hyperedge HE-6 (induced by source paper "ams without 4 wise independence on product domains"):
  with members:
    - "tight lower bounds for query processing on streaming and external memory data"

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)