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: "pattern matching in lempel ziv compressed strings fast simple and deterministic"
  Abstract: Countless variants of the Lempel-Ziv compression are widely used in many real-life applications. This paper is concerned with a natural modification of the classical pattern matching problem inspired by the popularity of such compression methods: given an uncompressed pattern s[1..m] and a Lempel-Ziv representation of a string t[1..N], does s occur in t? Farach and Thorup gave a randomized O(nlog^2(N/n)+m) time solution for this problem, where n is the size of the compressed representation of t. We improve their result by developing a faster and fully deterministic O(nlog(N/n)+m) time algorithm with the same space complexity. Note that for highly compressible texts, log(N/n) might be of order n, so for such inputs the improvement is very significant. A (tiny) fragment of our method can be used to give an asymptotically optimal solution for the substring hashing problem considered by Farach and Muthukrishnan.

Connected through hyperedge HE-1 (induced by source paper "optimal time dictionary compressed indexes"):
  with members:
    - "optimal rank and select queries on dictionary compressed text"
    - "fast prefix search in little space with applications"
    - "universal compressed text indexing"
    - "representing the suffix tree with the cdawg"
    - "time space trade offs for lempel ziv compressed indexing"
    - "at the roots of dictionary compression string attractors"
    - "compressed indexing with signature grammars"
    - "document listing on repetitive collections with guaranteed performance"

Connected through hyperedge HE-2 (induced by source paper "on the approximation ratio of greedy parsings"):
  with members:
    - "optimal time dictionary compressed indexes"
    - "at the roots of dictionary compression string attractors"
    - "compression with the tudocomp framework"
    - "optimal time text indexing in bwt runs bounded space"
    - "a separation between run length slps and lz77"
    - "fully dynamic data structure for lce queries in compressed space"
    - "composite repetition aware data structures"
    - "factorizations of the fibonacci infinite word"

Connected through hyperedge HE-3 (induced by source paper "decompressing lempel ziv compressed text"):
  with members:
    - "mergeable dictionaries with shifts"
    - "time space trade offs for lempel ziv compressed indexing"
    - "lempel ziv decoding in external memory"
    - "approximating lz77 via small space multiple pattern matching"
    - "linear time lempel ziv factorization simple fast small"

Connected through hyperedge HE-4 (induced by source paper "optimal time text indexing in bwt runs bounded space"):
  with members:
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "fast label extraction in the cdawg"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "fully dynamic data structure for lce queries in compressed space"
    - "universal indexes for highly repetitive document collections"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "composite repetition aware data structures"
    - "computing lz77 in run compressed space"

Connected through hyperedge HE-5 (induced by source paper "comparison of lz77 type parsings"):
  with members:
    - "at the roots of dictionary compression string attractors"
    - "relations between greedy and bit optimal lz77 encodings"
    - "optimal time text indexing in bwt runs bounded space"
    - "a separation between run length slps and lz77"
    - "on the size of lempel ziv and lyndon factorizations"
    - "approximation of grammar based compression via recompression"

Connected through hyperedge HE-6 (induced by source paper "longest gapped repeats and palindromes"):
  with members:
    - "optimal searching of gapped repeats in a word"
    - "lempel ziv factorization may be harder than computing all runs"
    - "searching of gapped repeats and subrepetitions in a word"
    - "a minimal periods algorithm with applications"

Connected through hyperedge HE-7 (induced by source paper "a really simple approximation of smallest grammar"):
  with members:
    - "approximation of grammar based compression via recompression"
    - "approximation of smallest linear tree grammar"
    - "linear time lempel ziv factorization simple fast small"
    - "simpler and faster lempel ziv factorization"
    - "faster fully compressed pattern matching by recompression"
    - "the similarity metric"
    - "identifying hierarchical structure in sequences a linear time algorithm"

Connected through hyperedge HE-8 (induced by source paper "the complexity of compressed membership problems for finite automata"):
  with members:
    - "recompression a simple and powerful technique for word equations"
    - "tying up the loose ends in fully lzw compressed pattern matching"
    - "faster fully compressed pattern matching by recompression"
    - "random access to grammar 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)