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: "hybrid indexes for repetitive datasets"
  Abstract: Advances in DNA sequencing mean that databases of thousands of human genomes will soon be commonplace. In this paper, we introduce a simple technique for reducing the size of conventional indexes on such highly repetitive texts. Given upper bounds on pattern lengths and edit distances, we pre-process the text with the lossless data compression algorithm LZ77 to obtain a filtered text, for which we store a conventional index. Later, given a query, we find all matches in the filtered text, then use their positions and the structure of the LZ77 parse to find all matches in the original text. Our experiments show that this also significantly reduces query times.

Connected through hyperedge HE-1 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space"):
  with members:
    - "resolution of the burrows wheeler transform conjecture"
    - "faster repetition aware compressed suffix trees based on block trees"
    - "optimal time dictionary compressed indexes"
    - "fast prefix search in little space with applications"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "on the approximation ratio of greedy parsings"
    - "universal compressed text indexing"
    - "prefix free parsing for building big bwts"

Connected through hyperedge HE-2 (induced by source paper "efficient construction of a complete index for pan genomics read alignment"):
  with members:
    - "prefix free parsing for building big bwts"
    - "optimal time text indexing in bwt runs bounded space"
    - "kmc 2 fast and resource frugal k mer counting"
    - "searching and indexing genomic databases via kernelization"
    - "indexes of large genome collections on a pc"

Connected through hyperedge HE-3 (induced by source paper "a compressed dynamic self index for highly repetitive text collections"):
  with members:
    - "optimal time dictionary compressed indexes"
    - "universal compressed text indexing"
    - "small space encoding lce data structure with constant time queries"
    - "time space trade offs for lempel ziv compressed indexing"
    - "at the roots of dictionary compression string attractors"
    - "compressed indexing with signature grammars"
    - "optimal time text indexing in bwt runs bounded space"
    - "a self index on block trees"

Connected through hyperedge HE-4 (induced by source paper "lempel ziv decoding in external memory"):
  with members:
    - "lempel ziv parsing in external memory"
    - "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 "fm index of alignment with gaps"):
  with members:
    - "from theory to practice plug and play with succinct data structures"

Connected through hyperedge HE-6 (induced by source paper "searching and indexing genomic databases via kernelization"):
  with members:
    - "fully online grammar compression in constant space"
    - "improved esp index a practical self index for highly repetitive texts"
    - "indexes of large genome collections on a pc"
    - "data structure lower bounds on random access to grammar compressed strings"
    - "faster approximate pattern matching in compressed repetitive texts"
    - "improved grammar based compressed indexes"
    - "random access to grammar compressed strings"

Connected through hyperedge HE-7 (induced by source paper "reusing an fm index"):
  with members:
    - "from theory to practice plug and play with succinct data structures"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-8 (induced by source paper "indexes of large genome collections on a pc"):
  with members:
    - "sampling the suffix array with minimizers"

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)