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: "improved esp index a practical self index for highly repetitive texts"
  Abstract: While several self-indexes for highly repetitive texts exist, developing a practical self-index applicable to real world repetitive texts remains a challenge. ESP-index is a grammar-based self-index on the notion of edit-sensitive parsing (ESP), an efficient parsing algorithm that guarantees upper bounds of parsing discrepancies between different appearances of the same subtexts in a text. Although ESP-index performs efficient top-down searches of query texts, it has a serious issue on binary searches for finding appearances of variables for a query text, which resulted in slowing down the query searches. We present an improved ESP-index (ESP-index-I) by leveraging the idea behind succinct data structures for large alphabets. While ESP-index-I keeps the same types of efficiencies as ESP-index about the top-down searches, it avoid the binary searches using fast rank/select operations. We experimentally test ESP-index-I on the ability to search query texts and extract subtexts from real world repetitive texts on a large-scale, and we show that ESP-index-I performs better that other possible approaches.

Connected through hyperedge HE-1 (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-2 (induced by source paper "linear size cdawg new repetition aware indexing and grammar compression"):
  with members:
    - "a self index on block trees"
    - "composite repetition aware data structures"

Connected through hyperedge HE-3 (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"
    - "searching and indexing genomic databases via kernelization"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-4 (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"
    - "restructuring compressed texts without explicit decompression"

Connected through hyperedge HE-5 (induced by source paper "online self indexed grammar compression"):
  with members:
    - "fully online grammar compression in constant space"
    - "improved grammar based compressed indexes"
    - "a faster grammar based self index"
    - "fully functional static and dynamic succinct trees"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

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"
    - "hybrid indexes for repetitive datasets"
    - "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"

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)