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: "beating countsketch for heavy hitters in insertion streams"
  Abstract: Given a stream $p_1, \ldots, p_m$ of items from a universe $\mathcal{U}$, which, without loss of generality we identify with the set of integers $\{1, 2, \ldots, n\}$, we consider the problem of returning all $\ell_2$-heavy hitters, i.e., those items $j$ for which $f_j \geq \epsilon \sqrt{F_2}$, where $f_j$ is the number of occurrences of item $j$ in the stream, and $F_2 = \sum_{i \in [n]} f_i^2$. Such a guarantee is considerably stronger than the $\ell_1$-guarantee, which finds those $j$ for which $f_j \geq \epsilon m$. In 2002, Charikar, Chen, and Farach-Colton suggested the {\sf CountSketch} data structure, which finds all such $j$ using $\Theta(\log^2 n)$ bits of space (for constant $\epsilon > 0$). The only known lower bound is $\Omega(\log n)$ bits of space, which comes from the need to specify the identities of the items found. In this paper we show it is possible to achieve $O(\log n \log \log n)$ bits of space for this problem. Our techniques, based on Gaussian processes, lead to a number of other new results for data streams, including #R##N#(1) The first algorithm for estimating $F_2$ simultaneously at all points in a stream using only $O(\log n\log\log n)$ bits of space, improving a natural union bound and the algorithm of Huang, Tai, and Yi (2014). #R##N#(2) A way to estimate the $\ell_{\infty}$ norm of a stream up to additive error $\epsilon \sqrt{F_2}$ with $O(\log n\log\log n)$ bits of space, resolving Open Question 3 from the IITK 2006 list for insertion only streams.

Connected through hyperedge HE-1 (induced by source paper "rambo repeated and merged bloom filter for multiple set membership testing msmt in sub linear time"):
  with members:
    - "extreme classification in log memory"

Connected through hyperedge HE-2 (induced by source paper "data streams with bounded deletions"):
  with members:
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "maximum matching in turnstile streams"
    - "stream sampling for frequency cap statistics"
    - "structure aware sampling flexible and accurate summarization"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-3 (induced by source paper "perfect l_p sampling in a data stream"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "heavy hitters via cluster preserving clustering"
    - "pseudorandomness via the discrete fourier transform"
    - "stream sampling for frequency cap statistics"
    - "improved concentration bounds for count sketch"
    - "structure aware sampling flexible and accurate summarization"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-4 (induced by source paper "nearly optimal distinct elements and heavy hitters on sliding windows"):
  with members:
    - "optimal streaming and tracking distinct elements with high probability"
    - "continuous monitoring of ell_p norms in data streams"
    - "submodular optimization over sliding windows"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "submodular maximization over sliding windows"
    - "heavy hitters via cluster preserving clustering"
    - "how to catch l 2 heavy hitters on sliding windows"
    - "sketching and streaming entropy via approximation theory"

Connected through hyperedge HE-5 (induced by source paper "the online event detection problem"):
  with members:
    - "optimal hashing in external memory"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "heavy hitters via cluster preserving clustering"
    - "communication complexity for algorithm designers"
    - "don t thrash how to cache your hash on flash"

Connected through hyperedge HE-6 (induced by source paper "tracking the ell_2 norm with constant update time"):
  with members:
    - "time lower bounds for nonadaptive turnstile streaming algorithms"
    - "tracking the frequency moments at all times"
    - "a sparse johnson lindenstrauss transform"
    - "fast moment estimation in data streams in optimal space"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-7 (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"
    - "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"
    - "measuring independence of datasets"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-8 (induced by source paper "a high performance algorithm for identifying frequent items in data streams"):
  with members:
    - "optimal elephant flow detection"
    - "constant time updates in hierarchical heavy hitters"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "smoking out the heavy hitter flows with hashpipe"
    - "simple and deterministic matrix sketching"
    - "hierarchical heavy hitters with the space saving algorithm"

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)