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: "lempel ziv parsing in external memory"
  Abstract: For decades, computing the LZ factorization (or LZ77 parsing) of a string has been a requisite and computationally intensive step in many diverse applications, including text indexing and data compression. Many algorithms for LZ77 parsing have been discovered over the years; however, despite the increasing need to apply LZ77 to massive data sets, no algorithm to date scales to inputs that exceed the size of internal memory. In this paper we describe the first algorithm for computing the LZ77 parsing in external memory. Our algorithm is fast in practice and will allow the next generation of text indexes to be realised for massive strings and string collections.

Connected through hyperedge HE-1 (induced by source paper "rpair rescaling repair with rsync"):
  with members:
    - "gract a grammar based compressed index for trajectory data"
    - "mr repair grammar compression based on maximal repeats"
    - "prefix free parsing for building big bwts"
    - "time space trade offs for lempel ziv compressed indexing"
    - "compressed indexing with signature grammars"
    - "universal indexes for highly repetitive document collections"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "a really simple approximation of smallest grammar"

Connected through hyperedge HE-2 (induced by source paper "lempel ziv like parsing in small space"):
  with members:
    - "at the roots of dictionary compression string attractors"
    - "relations between greedy and bit optimal lz77 encodings"
    - "lz end parsing in compressed space"
    - "approximating lz77 via small space multiple pattern matching"
    - "from theory to practice plug and play with succinct data structures"
    - "linear time lempel ziv factorization simple fast small"
    - "relative lempel ziv factorization for efficient storage and retrieval of web collections"
    - "large alphabets and incompressibility"

Connected through hyperedge HE-3 (induced by source paper "bidirectional text compression in external memory"):
  with members:
    - "lzrr lz77 parsing with right reference"
    - "o n log n time text compression by lz style longest first substitution"
    - "at the roots of dictionary compression string attractors"
    - "compression with the tudocomp framework"
    - "lempel ziv decoding in external memory"
    - "lz end parsing in compressed space"
    - "lightweight lempel ziv parsing"
    - "simpler and faster lempel ziv factorization"

Connected through hyperedge HE-4 (induced by source paper "lempel ziv decoding in external memory"):
  with members:
    - "hybrid indexes for repetitive datasets"
    - "faster approximate pattern matching in compressed repetitive texts"
    - "relative lempel ziv factorization for efficient storage and retrieval of web collections"
    - "grammar based compression in a streaming model"

Connected through hyperedge HE-5 (induced by source paper "lz end parsing in compressed space"):
  with members:
    - "faster lightweight lempel ziv parsing"
    - "range predecessor and lempel ziv parsing"
    - "approximating lz77 via small space multiple pattern matching"
    - "lightweight lempel ziv parsing"

Connected through hyperedge HE-6 (induced by source paper "approximating lz77 in small space"):
  with members:
    - "fully online grammar compression in constant space"
    - "searching and indexing genomic databases via kernelization"

Connected through hyperedge HE-7 (induced by source paper "lempel ziv factorization may be harder than computing all runs"):
  with members:
    - "searching of gapped repeats and subrepetitions in a word"
    - "faster compact on line lempel ziv factorization"
    - "on primary and secondary repetitions in words"
    - "on the maximal sum of exponents of runs in a string"

Connected through hyperedge HE-8 (induced by source paper "alibi an alignment based index for genomic datasets"):
  with members:
    - "hybrid indexes for repetitive datasets"
    - "data structure lower bounds on random access to grammar compressed strings"
    - "orthogonal range searching on the ram revisited"
    - "improved grammar based compressed indexes"
    - "a faster grammar based self index"

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)