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: "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
  Abstract: Document retrieval is one of the best established information retrieval activities since the sixties, pervading all search engines. Its aim is to obtain, from a collection of text documents, those most relevant to a pattern query. Current technology is mostly oriented to "natural language" text collections, where inverted indices are the preferred solution. As successful as this paradigm has been, it fails to properly handle some East Asian languages and other scenarios where the "natural language" assumptions do not hold. In this survey we cover the recent research in extending the document retrieval techniques to a broader class of sequence collections, which has applications bioinformatics, data and Web mining, chemoinformatics, software engineering, multimedia information retrieval, and many others. We focus on the algorithmic aspects of the techniques, uncovering a rich world of relations between document retrieval challenges and fundamental problems on trees, strings, range queries, discrete geometry, and others.

Connected through hyperedge HE-1 (induced by source paper "fast small and simple document listing on repetitive text collections"):
  with members:
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "document listing on repetitive collections with guaranteed performance"
    - "optimal time text indexing in bwt runs bounded space"
    - "document retrieval on repetitive string collections"
    - "document retrieval on repetitive collections"
    - "a really simple approximation of smallest grammar"

Connected through hyperedge HE-2 (induced by source paper "document listing on repetitive collections with guaranteed performance"):
  with members:
    - "fast prefix search in little space with applications"
    - "universal compressed text indexing"
    - "time space trade offs for lempel ziv compressed indexing"
    - "compressed indexing with signature grammars"
    - "optimal time text indexing in bwt runs bounded space"
    - "document retrieval on repetitive string collections"
    - "fully dynamic data structure for lce queries in compressed space"
    - "composite repetition aware data structures"

Connected through hyperedge HE-3 (induced by source paper "grammar compressed sequences with rank select support"):
  with members:
    - "queries on lz bounded encodings"
    - "a succinct grammar compression"
    - "compact binary relation representations with rich functionality"
    - "data structure lower bounds on random access to grammar compressed strings"
    - "optimal lower and upper bounds for representing sequences"
    - "on compressing permutations and adaptive sorting"
    - "optimal trade off for succinct string indexes"
    - "efficient fully compressed sequence representations"

Connected through hyperedge HE-4 (induced by source paper "a self index on block trees"):
  with members:
    - "universal indexes for highly repetitive document collections"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "queries on lz bounded encodings"
    - "a really simple approximation of smallest grammar"
    - "approximation of grammar based compression via recompression"
    - "orthogonal range searching on the ram revisited"
    - "improved grammar based compressed indexes"
    - "succinct representations of permutations and functions"

Connected through hyperedge HE-5 (induced by source paper "document retrieval on repetitive string collections"):
  with members:
    - "universal indexes for highly repetitive document collections"
    - "composite repetition aware data structures"
    - "document retrieval on repetitive collections"
    - "from theory to practice plug and play with succinct data structures"
    - "faster compact top k document retrieval"
    - "improved grammar based compressed indexes"
    - "new algorithms on wavelet trees and applications to information retrieval"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-6 (induced by source paper "rank select and access in grammar compressed strings"):
  with members:
    - "compressed subsequence matching and packed tree coloring"
    - "compact q gram profiling of compressed strings"
    - "data structure lower bounds on random access to grammar compressed strings"
    - "improved grammar based compressed indexes"
    - "fast q gram mining on slp compressed strings"
    - "unified compression based acceleration of edit distance computation"
    - "random access to grammar compressed strings"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-7 (induced by source paper "compact indexes for flexible top k retrieval"):
  with members:
    - "from theory to practice plug and play with succinct data structures"
    - "quasi succinct indices"
    - "decoding billions of integers per second through vectorization"
    - "faster compact top k document retrieval"
    - "new algorithms on wavelet trees and applications to information retrieval"

Connected through hyperedge HE-8 (induced by source paper "document counting in practice"):
  with members:
    - "document retrieval on repetitive collections"
    - "from theory to practice plug and play with succinct data structures"
    - "practical entropy compressed rank select dictionary"

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.IR(Information Retrieval)