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: "streaming algorithms for computing edit distance without exploiting suffix trees"
  Abstract: The edit distance is a way of quantifying how similar two strings are to one another by counting the minimum number of character insertions, deletions, and substitutions required to transform one string into the other. #R##N#In this paper we study the computational problem of computing the edit distance between a pair of strings where their distance is bounded by a parameter $k\ll n$. We present two streaming algorithms for computing edit distance: One runs in time $O(n+k^2)$ and the other $n+O(k^3)$. By writing $n+O(k^3)$ we want to emphasize that the number of operations per an input symbol is a small constant. In particular, the running time does not depend on the alphabet size, and the algorithm should be easy to implement. #R##N#Previously a streaming algorithm with running time $O(n+k^4)$ was given in the paper by the current authors (STOC'16). The best off-line algorithm runs in time $O(n+k^2)$ (Landau et al., 1998) which is known to be optimal under the Strong Exponential Time Hypothesis.

Connected through hyperedge HE-1 (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"
    - "on estimating edit distance alignment dimension reduction and embeddings"
    - "locality sensitive hashing of curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "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-2 (induced by source paper "sublinear algorithms for gap edit distance"):
  with members:
    - "approximating lcs in linear time beating the sqrt n barrier"
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "edit distance sketching streaming and document exchange"
    - "linear time sequence comparison using minimal absent words applications"
    - "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-3 (induced by source paper "sketching streaming and fine grained complexity of weighted lcs"):
  with members:
    - "faster str ic lcs computation via rle"
    - "edit distance sketching streaming and document exchange"
    - "which regular expression patterns are hard to match"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"

Connected through hyperedge HE-4 (induced by source paper "longest alignment with edits in data streams"):
  with members:
    - "streaming for aibohphobes longest palindrome with mismatches"
    - "tight tradeoffs for real time approximation of longest palindromes in streams"
    - "edit distance sketching streaming and document exchange"
    - "longest common substrings with k mismatches"
    - "the k mismatch problem revisited"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "sublinear space algorithms for the longest common substring problem"
    - "palindrome recognition in the streaming model"

Connected through hyperedge HE-5 (induced by source paper "embedjoin efficient edit similarity joins via embeddings"):
  with members:
    - "edit distance sketching streaming and document exchange"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "pass join a partition based method for similarity joins"
    - "bayesian locality sensitive hashing for fast similarity search"

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)