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: "space efficient streaming algorithms for the distance to monotonicity and asymmetric edit distance"
  Abstract: Approximating the length of the longest increasing sequence (LIS) of an array is a well-studied problem. We study this problem in the data stream model, where the algorithm is allowed to make a single left-to-right pass through the array and the key resource to be minimized is the amount of additional memory used. We present an algorithm which, for any $\delta > 0$, given streaming access to an array of length $n$ provides a $(1+\delta)$-multiplicative approximation to the \emph{distance to monotonicity} ($n$ minus the length of the LIS), and uses only $O((\log^2 n)/\delta)$ space. The previous best known approximation using polylogarithmic space was a multiplicative 2-factor. Our algorithm can be used to estimate the length of the LIS to within an additive $\delta n$ for any $\delta >0$ while previous algorithms could only achieve additive error $n(1/2-o(1))$. #R##N#Our algorithm is very simple, being just 3 lines of pseudocode, and has a small update time. It is essentially a polylogarithmic space approximate implementation of a classic dynamic program that computes the LIS. #R##N#We also give a streaming algorithm for approximating $LCS(x,y)$, the length of the longest common subsequence between strings $x$ and $y$, each of length $n$. Our algorithm works in the asymmetric setting (inspired by \cite{AKO10}), in which we have random access to $y$ and streaming access to $x$, and runs in small space provided that no single symbol appears very often in $y$. More precisely, it gives an additive-$\delta n$ approximation to $LCS(x,y)$ (and hence also to $E(x,y) = n-LCS(x,y)$, the edit distance between $x$ and $y$ when insertions and deletions, but not substitutions, are allowed), with space complexity $O(k(\log^2 n)/\delta)$, where $k$ is the maximum number of times any one symbol appears in $y$.

Connected through hyperedge HE-1 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs"):
  with members:
    - "approximating lcs in linear time beating the sqrt n barrier"
    - "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation"
    - "sublinear algorithms for gap edit distance"
    - "reducing approximate longest common subsequence to approximate edit distance"
    - "constant factor approximation of near linear edit distance in near linear time"
    - "constant factor approximations to edit distance on far input pairs in nearly linear time"
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "on estimating edit distance alignment dimension reduction and embeddings"

Connected through hyperedge HE-2 (induced by source paper "space efficient deterministic approximation of string measures"):
  with members:
    - "streaming with oracle new streaming algorithms for edit distance and lcs"
    - "approximating lcs in linear time beating the sqrt n barrier"
    - "constant factor approximation of near linear edit distance in near linear time"
    - "constant factor approximations to edit distance on far input pairs in nearly linear time"
    - "space efficient algorithms for longest increasing subsequence"
    - "the 2cnf boolean formula satisfiability problem and the linear space hypothesis"
    - "edit distance sketching streaming and document exchange"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

Connected through hyperedge HE-3 (induced by source paper "optimal adaptive detection of monotone patterns"):
  with members:
    - "finding and counting permutations via csps"
    - "finding monotone patterns in sublinear time"
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "adaptive boolean monotonicity testing in total influence time"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "improved bounds for testing forbidden order patterns"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"

Connected through hyperedge HE-4 (induced by source paper "finding monotone patterns in sublinear time"):
  with members:
    - "faster and simpler algorithms for finding large patterns in permutations"
    - "adaptive lower bound for testing monotonicity on the line"
    - "testing local properties of arrays"
    - "adaptive boolean monotonicity testing in total influence time"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "improved bounds for testing forbidden order patterns"
    - "boolean function monotonicity testing requires almost n 1 2 non adaptive queries"
    - "new algorithms and lower bounds for monotonicity testing"

Connected through hyperedge HE-5 (induced by source paper "space efficient algorithms for longest increasing subsequence"):
  with members:
    - "improved time space trade offs for computing voronoi diagrams"
    - "on space efficiency of algorithms working on structural decompositions of graphs"
    - "estimating the longest increasing sequence in polylogarithmic time"

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)