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: "resolution of the burrows wheeler transform conjecture"
  Abstract: Burrows-Wheeler Transform (BWT) is an invertible text transformation that permutes symbols of a text according to the lexicographical order of its suffixes. BWT is the main component of some of the most popular lossless compression methods as well as of compressed indexes, central in modern bioinformatics. The compression ratio of BWT-based compressors, such as bzip2, is quantified by the number $r$ of maximal equal-letter runs in the BWT. This is also (up to ${\rm polylog}\,n$ factors, where $n$ is the length of the text) the space used by the state-of-the-art BWT-based indexes, such as the recent $r$-index [Gagie et al., SODA 2018]. The output size of virtually every known compression method is known to be either within a ${\rm polylog}\,n$ factor from $z$, the size of Lempel-Ziv (LZ77) parsing of the text, or significantly larger (by a $n^{\epsilon}$ factor for $\epsilon > 0$). The value of $r$ has resisted, however, all attempts and until now, no non-trivial upper bounds on $r$ were known. #R##N#In this paper, we show that every text satisfies $r=\mathcal{O}(z\log^2 n)$. This result has a number of immediate implications: (1) it proves that a large body of work related to BWT automatically applies to the so-far disjoint field of Lempel--Ziv indexing and compression, e.g., it is possible to obtain full functionality of the suffix tree and the suffix array in $\mathcal{O}(z\,{\rm polylog}\,n)$ space; (2) it lets us relate the number of runs in the BWT of the text and its reverse; (3) it shows that many fundamental text processing tasks can be solved in the optimal time assuming that the text is compressible by a sufficiently large ${\rm polylog}\,n$ factor using LZ77.

Connected through hyperedge HE-1 (induced by source paper "on extensions of maximal repeats in compressed strings"):
  with members:
    - "mr repair grammar compression based on maximal repeats"
    - "composite repetition aware data structures"

Connected through hyperedge HE-2 (induced by source paper "towards a definitive measure of repetitiveness"):
  with members:
    - "optimal rank and select queries on dictionary compressed text"
    - "optimal time dictionary compressed indexes"
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "on the approximation ratio of greedy parsings"
    - "universal compressed text indexing"
    - "representing the suffix tree with the cdawg"
    - "time space trade offs for lempel ziv compressed indexing"
    - "at the roots of dictionary compression string attractors"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering"):
  with members:
    - "on the hardness and inapproximability of recognizing wheeler graphs"
    - "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
    - "tunneling on wheeler graphs"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "at the roots of dictionary compression string attractors"
    - "optimal time text indexing in bwt runs bounded space"
    - "space efficient construction of compressed indexes in deterministic linear time"

Connected through hyperedge HE-4 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space"):
  with members:
    - "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"
    - "representing the suffix tree with the cdawg"

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)