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: "efficiently computing edit distance to dyck language"
  Abstract: Given a string $\sigma$ over alphabet $\Sigma$ and a grammar $G$ defined over the same alphabet, how many minimum number of repairs: insertions, deletions and substitutions are required to map $\sigma$ into a valid member of $G$ ? We investigate this basic question in this paper for $Dyck(s)$. $Dyck(s)$ is a fundamental context free grammar representing the language of well-balanced parentheses with s different types of parentheses and has played a pivotal role in the development of theory of context free languages. Computing edit distance to $Dyck(s)$ significantly generalizes string edit distance problem and has numerous applications ranging from repairing semi-structured documents such as XML to memory checking, automated compiler optimization, natural language processing etc. #R##N#In this paper we give the first near-linear time algorithm for edit distance computation to $Dyck(s)$ that achieves a nontrivial approximation factor of $O(\frac{1}{\epsilon}\log{OPT}(\log{n})^{\frac{1}{\epsilon}})$ in $O(n^{1+\epsilon}\log{n})$ time. In fact, given there exists an algorithm for computing string edit distance on input of size $n$ in $\alpha(n)$ time with $\beta(n)$-approximation factor, we can devise an algorithm for edit distance problem to $Dyck(s)$ running in $\tilde{O}(n^{1+\epsilon}+\alpha(n))$ and achieving an approximation factor of $O(\frac{1}{\epsilon}\beta(n)\log{OPT})$. #R##N#We show that the framework for efficiently approximating edit distance to $Dyck(s)$ can be applied to many other languages. We illustrate this by considering various memory checking languages which comprise of valid transcripts of stacks, queues, priority queues, double-ended queues etc. Therefore, any language that can be recognized by these data structures, can also be repaired efficiently by our algorithm.

Connected through hyperedge HE-1 (induced by source paper "an error correcting parser for context free grammars that takes less than cubic time"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "faster all pairs shortest paths via circuit complexity"
    - "fast context free grammar parsing requires fast boolean matrix multiplication"

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)