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: "repair in compressed space and time"
  Abstract: Given a string $T$ of length $N$, the goal of grammar compression is to construct a small context-free grammar generating only $T$. Among existing grammar compression methods, RePair (recursive paring) [Larsson and Moffat, 1999] is notable for achieving good compression ratios in practice. Although the original paper already achieved a time-optimal algorithm to compute the RePair grammar RePair($T$) in expected $O(N)$ time, the study to reduce its working space is still active so that it is applicable to large-scale data. In this paper, we propose the first RePair algorithm working in compressed space, i.e., potentially $o(N)$ space for highly compressible texts. The key idea is to give a new way to restructure an arbitrary grammar $S$ for $T$ into RePair($T$) in compressed space and time. Based on the recompression technique, we propose an algorithm for RePair($T$) in $O(\min(N, nm \log N))$ space and expected $O(\min(N, nm \log N) m)$ time or $O(\min(N, nm \log N) \log \log N)$ time, where $n$ is the size of $S$ and $m$ is the number of variables in RePair($T$). We implemented our algorithm running in $O(\min(N, nm \log N) m)$ time and show it can actually run in compressed space. We also present a new approach to reduce the peak memory usage of existing RePair algorithms combining with our algorithms, and show that the new approach outperforms, both in computation time and space, the most space efficient linear-time RePair implementation to date.

Connected through hyperedge HE-1 (induced by source paper "re pair in place"):
  with members:
    - "a study for image compression using re pair algorithm"
    - "a faster external memory priority queue with decreasekeys"
    - "mr repair grammar compression based on maximal repeats"
    - "burrows wheeler transform and lcp array construction in constant space"
    - "practical and effective re pair compression"
    - "optimal time and space construction of suffix arrays and lcp arrays for integer alphabets"
    - "optimal in place suffix sorting"
    - "scalable partial least squares regression on grammar compressed data matrices"

Connected through hyperedge HE-2 (induced by source paper "practical repetition aware grammar compression"):
  with members:
    - "mr repair grammar compression based on maximal repeats"
    - "optimal time text indexing in bwt runs bounded space"
    - "scalable partial least squares regression on grammar compressed data matrices"
    - "space efficient re pair compression"
    - "online pattern matching for string edit distance with moves"
    - "compact q gram profiling of compressed strings"
    - "a succinct grammar compression"
    - "approximation of grammar based compression via recompression"

Connected through hyperedge HE-3 (induced by source paper "conversion from rlbwt to lz77"):
  with members:
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "dynamic relative compression dynamic partial sums and substring concatenation"
    - "a really simple approximation of smallest grammar"
    - "alphabet dependent string searching with wexponential search trees"

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)