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: "time space trade offs for lempel ziv compressed indexing"
  Abstract: Abstract   Given a string S, the compressed indexing problem is to preprocess S into a compressed representation that supports fast substring queries. The goal is to use little space relative to the compressed size of S while supporting fast queries. We present a compressed index based on the Lempel–Ziv 1977 compression scheme. We obtain the following time–space trade-offs: For constant-sized alphabets  (i)      O  (  m  +   occ   lg  ⁡  lg  ⁡  n  )    time using    O  (  z  lg  ⁡  (  n  /  z  )  lg  ⁡  lg  ⁡  z  )    space, or   (ii)      O  (  m  (  1  +      lg    ϵ    ⁡  z    lg  ⁡  (  n  /  z  )    )  +   occ   (  lg  ⁡  lg  ⁡  n  +    lg    ϵ    ⁡  z  )  )    time using    O  (  z  lg  ⁡  (  n  /  z  )  )    space,    For integer alphabets polynomially bounded by n  (iii)      O  (  m  (  1  +      lg    ϵ    ⁡  z    lg  ⁡  (  n  /  z  )    )  +   occ   (  lg  ⁡  lg  ⁡  n  +    lg    ϵ    ⁡  z  )  )    time using    O  (  z  (  lg  ⁡  (  n  /  z  )  +  lg  ⁡  lg  ⁡  z  )  )    space, or   (iv)      O  (  m  +   occ   (  lg  ⁡  lg  ⁡  n  +    lg    ϵ    ⁡  z  )  )    time using    O  (  z  (  lg  ⁡  (  n  /  z  )  +    lg    ϵ    ⁡  z  )  )    space,    where n and m are the length of the input string and query string respectively, z is the number of phrases in the LZ77 parse of the input string, occ is the number of occurrences of the query in the input and    ϵ  >  0    is an arbitrarily small constant. In particular, (i) improves the leading term in the query time of the previous best solution from    O  (  m  lg  ⁡  m  )    to    O  (  m  )    at the cost of increasing the space by a factor    lg  ⁡  lg  ⁡  z   . Alternatively, (ii) matches the previous best space bound, but has a leading term in the query time of    O  (  m  (  1  +      lg    ϵ    ⁡  z    lg  ⁡  (  n  /  z  )    )  )   . However, for any polynomial compression ratio, i.e.,    z  =  O  (    n    1  −  δ    )   , for constant    δ  >  0   , this becomes    O  (  m  )   . Our index also supports extraction of any substring of length l in    O  (  l  +  lg  ⁡  (  n  /  z  )  )    time. Technically, our results are obtained by novel extensions and combinations of existing data structures of independent interest, including a new batched variant of weak prefix search.

Connected through hyperedge HE-1 (induced by source paper "top tree compression of tries"):
  with members:
    - "dynamic packed compact tries revisited"
    - "universal compressed text indexing"
    - "packed compact tries a fast and efficient data structure for online string processing"
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "compressed indexing with signature grammars"
    - "deterministic indexing for packed strings"
    - "composite repetition aware data structures"
    - "tree compression with top trees revisited"

Connected through hyperedge HE-2 (induced by source paper "towards a definitive measure of repetitiveness"):
  with members:
    - "resolution of the burrows wheeler transform conjecture"
    - "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"
    - "at the roots of dictionary compression string attractors"

Connected through hyperedge HE-3 (induced by source paper "rpair rescaling repair with rsync"):
  with members:
    - "gract a grammar based compressed index for trajectory data"
    - "mr repair grammar compression based on maximal repeats"
    - "prefix free parsing for building big bwts"
    - "compressed indexing with signature grammars"
    - "universal indexes for highly repetitive document collections"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "a really simple approximation of smallest grammar"
    - "approximation of grammar based compression via recompression"

Connected through hyperedge HE-4 (induced by source paper "resolution of the burrows wheeler transform conjecture"):
  with members:
    - "towards a definitive measure of repetitiveness"
    - "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"
    - "universal compressed text indexing"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "at the roots of dictionary compression string attractors"
    - "queries on lz bounded encodings"

Connected through hyperedge HE-5 (induced by source paper "string indexing with compressed patterns"):
  with members:
    - "longest common extensions in sublinear space"
    - "searching and indexing genomic databases via kernelization"
    - "improved grammar based compressed indexes"

Connected through hyperedge HE-6 (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-7 (induced by source paper "universal compressed text indexing"):
  with members:
    - "fast prefix search in little space with applications"
    - "at the roots of dictionary compression string attractors"
    - "compressed indexing with signature grammars"
    - "fast label extraction in the cdawg"
    - "optimal time text indexing in bwt runs bounded space"
    - "sparse suffix tree construction in optimal time and space"
    - "a self index on block trees"
    - "dynamic index and lz factorization in compressed space"

Connected through hyperedge HE-8 (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"
    - "at the roots of dictionary compression string attractors"
    - "compressed indexing with signature grammars"
    - "document listing on repetitive collections with guaranteed performance"
    - "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)