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: "document listing on repetitive collections with guaranteed performance"
  Abstract: We consider document listing on string collections, that is, finding in which strings a given pattern appears. In particular, we focus on repetitive collections: a collection of size $N$ over alphabet $[1,\sigma]$ is composed of $D$ copies of a string of size $n$, and $s$ edits are applied on ranges of copies. We introduce the first document listing index with size $\tilde{O}(n+s)$, precisely $O((n\log\sigma+s\log^2 N)\log D)$ bits, and with useful worst-case time guarantees: Given a pattern of length $m$, the index reports the $\ndoc>0$ strings where it appears in time $O(m\log^{1+\epsilon} N \cdot \ndoc)$, for any constant $\epsilon>0$ (and tells in time $O(m\log N)$ if $\ndoc=0$). Our technique is to augment a range data structure that is commonly used on grammar-based indexes, so that instead of retrieving all the pattern occurrences, it computes useful summaries on them. We show that the idea has independent interest: we introduce the first grammar-based index that, on a text $T[1,N]$ with a grammar of size $r$, uses $O(r\log N)$ bits and counts the number of occurrences of a pattern $P[1,m]$ in time $O(m^2 + m\log^{2+\epsilon} r)$, for any constant $\epsilon>0$. We also give the first index using $O(z\log(N/z)\log N)$ bits, where $T$ is parsed by Lempel-Ziv into $z$ phrases, counting occurrences in time $O(m\log^{2+\epsilon} N)$.

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"
    - "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"
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"

Connected through hyperedge HE-2 (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-3 (induced by source paper "optimal time dictionary compressed indexes"):
  with members:
    - "optimal rank and select queries on dictionary compressed text"
    - "fast prefix search in little space with applications"
    - "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"
    - "compressed indexing with signature grammars"
    - "optimal time text indexing in bwt runs bounded space"

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)