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: "searching and indexing genomic databases via kernelization"
  Abstract: The rapid advance of DNA sequencing technologies has yielded databases of thousands of genomes. To search and index these databases effectively, it is important that we take advantage of the similarity between those genomes. Several authors have recently suggested searching or indexing only one reference genome and the parts of the other genomes where they differ. In this paper we survey the twenty-year history of this idea and discuss its relation to kernelization in parameterized complexity.

Connected through hyperedge HE-1 (induced by source paper "string indexing with compressed patterns"):
  with members:
    - "time space trade offs for lempel ziv compressed indexing"
    - "longest common extensions in sublinear space"
    - "improved grammar based compressed indexes"

Connected through hyperedge HE-2 (induced by source paper "refining the r index"):
  with members:
    - "conversion from rlbwt to lz77"
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "lower bounds for text indexing with mismatches and differences"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "prefix free parsing for building big bwts"
    - "from lz77 to the run length encoded burrows wheeler transform and back"
    - "a framework of dynamic data structures for string processing"
    - "optimal construction of compressed indexes for highly repetitive texts"

Connected through hyperedge HE-3 (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"
    - "hybrid indexes for repetitive datasets"
    - "indexes of large genome collections on a pc"

Connected through hyperedge HE-4 (induced by source paper "time space trade offs for lempel ziv compressed indexing"):
  with members:
    - "fast prefix search in little space with applications"
    - "universal indexes for highly repetitive document collections"
    - "composite repetition aware data structures"
    - "queries on lz bounded encodings"
    - "orthogonal range searching for text indexing"
    - "time space trade offs for longest common extensions"
    - "orthogonal range searching on the ram revisited"
    - "improved grammar based compressed indexes"

Connected through hyperedge HE-5 (induced by source paper "siedm an efficient string index and search algorithm for edit distance with moves"):
  with members:
    - "online self indexed grammar compression"
    - "fully online grammar compression in constant space"
    - "online pattern matching for string edit distance with moves"
    - "improved esp index a practical self index for highly repetitive texts"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-6 (induced by source paper "relative compressed suffix trees"):
  with members:
    - "indexing variation graphs"
    - "rlzap relative lempel ziv with adaptive pointers"
    - "composite repetition aware data structures"
    - "from theory to practice plug and play with succinct data structures"
    - "fixed block compression boosting in fm indexes"
    - "a faster grammar based self index"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "compressed text indexes from theory to practice"

Connected through hyperedge HE-7 (induced by source paper "approximating lz77 in small space"):
  with members:
    - "fully online grammar compression in constant space"
    - "lempel ziv parsing in external memory"

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)