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: "constant factor approximations to edit distance on far input pairs in nearly linear time"
  Abstract: For any $T \geq 1$, there are constants $R=R(T) \geq 1$ and $\zeta=\zeta(T)>0$ and a randomized algorithm that takes as input an integer $n$ and two strings $x,y$ of length at most $n$, and runs in time $O(n^{1+\frac{1}{T}})$ and outputs an upper bound $U$ on the edit distance $ED(x,y)$ that with high probability, satisfies $U \leq R(ED(x,y)+n^{1-\zeta})$. In particular, on any input with $ED(x,y) \geq n^{1-\zeta}$ the algorithm outputs a constant factor approximation with high probability. #R##N#A similar result has been proven independently by Brakensiek and Rubinstein (2019).

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"
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "on estimating edit distance alignment dimension reduction and embeddings"
    - "multivariate fine grained complexity of longest common subsequence"

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"
    - "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"
    - "space efficient streaming algorithms for the distance to monotonicity and asymmetric edit distance"

Connected through hyperedge HE-3 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing"):
  with members:
    - "constant factor approximation of near linear edit distance in near linear time"
    - "race sub linear memory sketches for approximate near neighbor search on streaming data"
    - "near linear time insertion deletion codes and 1 e approximating edit distance via indexing"
    - "approximate nearest neighbor search in high dimensions"
    - "set similarity search for skewed data"
    - "hardness of approximate nearest neighbor search"
    - "fast entropy bounded string dictionary look up with mismatches"
    - "lower bounds for text indexing with mismatches and differences"

Connected through hyperedge HE-4 (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"
    - "approximating edit distance within constant factor in truly sub quadratic time"
    - "on estimating edit distance alignment dimension reduction and embeddings"
    - "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-5 (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"
    - "on estimating edit distance alignment dimension reduction and embeddings"
    - "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)