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: "unified compression based acceleration of edit distance computation"
  Abstract: The edit distance problem is a classical fundamental problem in computer science in general, and in combinatorial pattern matching in particular. The standard dynamic programming solution for this problem computes the edit-distance between a pair of strings of total length O(N) in O(N^2) time. To this date, this quadratic upper-bound has never been substantially improved for general strings. However, there are known techniques for breaking this bound in case the strings are known to compress well under a particular compression scheme. The basic idea is to first compress the strings, and then to compute the edit distance between the compressed strings. As it turns out, practically all known o(N^2) edit-distance algorithms work, in some sense, under the same paradigm described above. It is therefore natural to ask whether there is a single edit-distance algorithm that works for strings which are compressed under any compression scheme. A rephrasing of this question is to ask whether a single algorithm can exploit the compressibility properties of strings under any compression method, even if each string is compressed using a different compression. In this paper we set out to answer this question by using straight line programs. These provide a generic platform for representing many popular compression schemes including the LZ-family, Run-Length Encoding, Byte-Pair Encoding, and dictionary methods. For two strings of total length N having straight-line program representations of total size n, we present an algorithm running in O(nN log(N/n)) time for computing the edit-distance of these two strings under any rational scoring function, and an O(n^{2/3}N^{4/3}) time algorithm for arbitrary scoring functions. Our new result, while providing a signi cant speed up for highly compressible strings, does not surpass the quadratic time bound even in the worst case scenario.

Connected through hyperedge HE-1 (induced by source paper "fine grained complexity of analyzing compressed data quantifying improvements over decompress and solve"):
  with members:
    - "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product"
    - "why is it hard to beat o n 2 for longest common weakly increasing subsequence"
    - "a dichotomy for regular expression membership testing"
    - "conditional lower bound for rna folding problem"
    - "the k mismatch problem revisited"
    - "clustered integer 3sum via additive combinatorics"
    - "which regular expression patterns are hard to match"
    - "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made"

Connected through hyperedge HE-2 (induced by source paper "rank select and access in grammar compressed strings"):
  with members:
    - "compressed subsequence matching and packed tree coloring"
    - "compact q gram profiling of compressed strings"
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "data structure lower bounds on random access to grammar compressed strings"
    - "improved grammar based compressed indexes"
    - "fast q gram mining on slp compressed strings"
    - "random access to grammar compressed strings"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-3 (induced by source paper "computing the discrete fr echet distance in subquadratic time"):
  with members:
    - "four soviets walk the dog with an application to alt s conjecture"
    - "jaywalking your dog computing the fr echet distance with shortcuts"

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)