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: "restructuring compressed texts without explicit decompression"
  Abstract: We consider the problem of {\em restructuring} compressed texts without explicit decompression. We present algorithms which allow conversions from compressed representations of a string $T$ produced by any grammar-based compression algorithm, to representations produced by several specific compression algorithms including LZ77, LZ78, run length encoding, and some grammar based compression algorithms. These are the first algorithms that achieve running times polynomial in the size of the compressed input and output representations of $T$. Since most of the representations we consider can achieve exponential compression, our algorithms are theoretically faster in the worst case, than any algorithm which first decompresses the string for the conversion.

Connected through hyperedge HE-1 (induced by source paper "repair in compressed space and time"):
  with members:
    - "entropy bounds for grammar compression"
    - "approximation ratio of repair"
    - "practical and effective re pair compression"
    - "approximation of smallest linear tree grammar"

Connected through hyperedge HE-2 (induced by source paper "dynamic index and lz factorization in compressed space"):
  with members:
    - "fully dynamic data structure for lce queries in compressed space"
    - "online self indexed grammar compression"
    - "dynamic data structures for document collections and graphs"
    - "improved esp index a practical self index for highly repetitive texts"

Connected through hyperedge HE-3 (induced by source paper "dynamic index lz factorization and lce queries in compressed space"):
  with members:
    - "online self indexed grammar compression"
    - "approximating lz77 via small space multiple pattern matching"
    - "dynamic data structures for document collections and graphs"
    - "finger search random access and longest common extensions in grammar compressed strings"
    - "fingerprints in compressed strings"

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)