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: "bptree an ell_2 heavy hitters algorithm using constant memory"
  Abstract: The task of finding heavy hitters is one of the best known and well studied problems in the area of data streams. One is given a list $i_1,i_2,\ldots,i_m\in[n]$ and the goal is to identify the items among $[n]$ that appear frequently in the list. In sub-polynomial space, the strongest guarantee available is the $\ell_2$ guarantee, which requires finding all items that occur at least $\epsilon\|f\|_2$ times in the stream, where the vector $f\in\mathbb{R}^n$ is the count histogram of the stream with $i$th coordinate equal to the number of times~$i$ appears $f_i:=\#\{j\in[m]:i_j=i\}$. The first algorithm to achieve the $\ell_2$ guarantee was the CountSketch of [CCF04], which requires $O(\epsilon^{-2}\log n)$ words of memory and $O(\log n)$ update time and is known to be space-optimal if the stream allows for deletions. The recent work of [BCIW16] gave an improved algorithm for insertion-only streams, using only $O(\epsilon^{-2}\log\epsilon^{-1}\log\log n)$ words of memory. In this work, we give an algorithm \bptree for $\ell_2$ heavy hitters in insertion-only streams that achieves $O(\epsilon^{-2}\log\epsilon^{-1})$ words of memory and $O(\log\epsilon^{-1})$ update time, which is the optimal dependence on $n$ and $m$. In addition, we describe an algorithm for tracking $\|f\|_2$ at all times with $O(\epsilon^{-2})$ memory and update time. Our analyses rely on bounding the expected supremum of a Bernoulli process involving Rademachers with limited independence, which we accomplish via a Dudley-like chaining argument that may have applications elsewhere.

Connected through hyperedge HE-1 (induced by source paper "towards optimal moment estimation in streaming and distributed models"):
  with members:
    - "optimal random sampling from distributed streams revisited"
    - "weighted reservoir sampling from distributed streams"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed statistical estimation of matrix products with applications"
    - "revisiting frequency moment estimation in random order streams"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "continuous monitoring of ell_p norms in data streams"
    - "communication optimal distributed clustering"

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"
    - "beating countsketch for heavy hitters in insertion streams"
    - "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 "universal streaming of subset norms"):
  with members:
    - "revisiting frequency moment estimation in random order streams"
    - "streaming space complexity of nearly all functions of one variable on frequency vectors"
    - "streaming symmetric norms via measure concentration"
    - "on sketching quadratic forms"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"
    - "fast moment estimation in data streams in optimal space"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "streaming algorithms from precision sampling"

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"
    - "submodular maximization over sliding windows"
    - "heavy hitters via cluster preserving clustering"
    - "beating countsketch for heavy hitters in insertion streams"
    - "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"
    - "heavy hitters via cluster preserving clustering"
    - "beating countsketch for heavy hitters in insertion streams"
    - "communication complexity for algorithm designers"
    - "don t thrash how to cache your hash on flash"

Connected through hyperedge HE-6 (induced by source paper "revisiting frequency moment estimation in random order streams"):
  with members:
    - "fast moment estimation in data streams in optimal space"
    - "streaming algorithms from precision sampling"
    - "recursive sketching for frequency moments"

Connected through hyperedge HE-7 (induced by source paper "continuous monitoring of ell_p norms in data streams"):
  with members:
    - "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"
    - "measuring independence of datasets"
    - "bounded independence fools degree 2 threshold functions"

Connected through hyperedge HE-8 (induced by source paper "new algorithms for heavy hitters in data streams"):
  with members:
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "beating countsketch for heavy hitters in insertion streams"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "improved concentration bounds for count sketch"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "lower bounds for sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

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)