human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "language edit distance maximum likelihood parsing of stochastic grammars faster algorithms connection to fundamental graph problems"):
  Abstract: Given a context free language $L(G)$ over alphabet $\Sigma$ and a string $s \in \Sigma^*$, the language edit distance (Lan-ED) problem seeks the minimum number of edits (insertions, deletions and substitutions) required to convert $s$ into a valid member of $L(G)$. The well-known dynamic programming algorithm solves this problem in $O(n^3)$ time (ignoring grammar size) where $n$ is the string length [Aho, Peterson 1972, Myers 1985]. Despite its vast number of applications, there is no algorithm known till date that computes or approximates Lan-ED in true sub-cubic time. #R##N#In this paper we give the first such algorithm that computes Lan-ED almost optimally. For any arbitrary $\epsilon > 0$, our algorithm runs in $\tilde{O}(\frac{n^{\omega}}{poly(\epsilon)})$ time and returns an estimate within a multiplicative approximation factor of $(1+\epsilon)$, where $\omega$ is the exponent of ordinary matrix multiplication of $n$ dimensional square matrices. It also computes the edit script. Further, for all substrings of $s$, we can estimate their Lan-ED within $(1\pm \epsilon)$ factor in $\tilde{O}(\frac{n^{\omega}}{poly(\epsilon)})$ time with high probability. We also design the very first sub-cubic ($\tilde{O}(n^\omega)$) algorithm to handle arbitrary stochastic context free grammar (SCFG) parsing. SCFGs lie at the foundation of statistical natural language processing, they generalize hidden Markov models, and have found widespread applications. #R##N#To complement our upper bound result, we show that exact computation of SCFG parsing, or Lan-ED with insertion as only edit operation in true sub-cubic time will imply a truly sub-cubic algorithm for all-pairs shortest paths, and hence to a large range of problems in graphs and matrices. Known lower bound results on parsing implies no improvement over our time bound of $O(n^\omega)$ is possible for any nontrivial multiplicative approximation.

Includes members:
  Member "edit distance cannot be computed in strongly subquadratic time unless seth is false":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "an error correcting parser for context free grammars that takes less than cubic time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reducing cascading parsing errors through fast error recovery")
  Member "faster all pairs shortest paths via circuit complexity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic algorithms for decremental approximate shortest paths faster and simpler")
  Member "faster algorithms for rectangular matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deterministic algorithms for decremental approximate shortest paths faster and simpler")
  Member "fast context free grammar parsing requires fast boolean matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal dyck reachability for data dependence and alias analysis")

Question: Please tell me which class the source paper 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)