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: "on estimating edit distance alignment dimension reduction and embeddings"
  Abstract: Edit distance is a fundamental measure of distance between strings and has been widely studied in computer science. While the problem of estimating edit distance has been studied extensively, the equally important question of actually producing an alignment (i.e., the sequence of edits) has received far less attention. Somewhat surprisingly, we show that any algorithm to estimate edit distance can be used in a black-box fashion to produce an approximate alignment of strings, with modest loss in approximation factor and small loss in run time. Plugging in the result of Andoni, Krauthgamer, and Onak, we obtain an alignment that is a $(\log n)^{O(1/\varepsilon^2)}$ approximation in time $\tilde{O}(n^{1 + \varepsilon})$. #R##N#Closely related to the study of approximation algorithms is the study of metric embeddings for edit distance. We show that min-hash techniques can be useful in designing edit distance embeddings through three results: (1) An embedding from Ulam distance (edit distance over permutations) to Hamming space that matches the best known distortion of $O(\log n)$ and also implicitly encodes a sequence of edits between the strings; (2) In the case where the edit distance between the input strings is known to have an upper bound $K$, we show that embeddings of edit distance into Hamming space with distortion $f(n)$ can be modified in a black-box fashion to give distortion $O(f(\operatorname{poly}(K)))$ for a class of periodic-free strings; (3) A randomized dimension-reduction map with contraction $c$ and asymptotically optimal expected distortion $O(c)$, improving on the previous $\tilde{O}(c^{1 + 2 / \log \log \log n})$ distortion result of Batu, Ergun, and Sahinalp.

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"
    - "multivariate fine grained complexity of longest common subsequence"

Connected through hyperedge HE-2 (induced by source paper "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation"):
  with members:
    - "the one way communication complexity of dynamic time warping distance"
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "locality sensitive hashing of curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "streaming algorithms for computing edit distance without exploiting suffix trees"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "approximating dynamic time warping and edit distance for a pair of point sequences"

Connected through hyperedge HE-3 (induced by source paper "reducing approximate longest common subsequence to approximate edit distance"):
  with members:
    - "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"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "fine grained complexity meets ip pspace"
    - "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

Connected through hyperedge HE-4 (induced by source paper "constant factor approximation of near linear edit distance in near linear time"):
  with members:
    - "reducing approximate longest common subsequence to approximate edit distance"
    - "constant factor approximations to edit distance on far input pairs in nearly linear time"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "approximating edit distance in near linear time"
    - "polylogarithmic approximation for edit distance and the asymmetric query complexity"

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)