human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "fast prefix search in little space with applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space")
  Member "universal indexes for highly repetitive document collections":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rpair rescaling repair with rsync")
  Member "composite repetition aware data structures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on extensions of maximal repeats in compressed strings")
  Member "searching and indexing genomic databases via kernelization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "string indexing with compressed patterns")
  Member "queries on lz bounded encodings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "top tree compression of tries")
  Member "orthogonal range searching for text indexing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "submatrix maximum queries in monge matrices are equivalent to predecessor search")
  Member "time space trade offs for longest common extensions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fingerprints in compressed strings")
  Member "orthogonal range searching on the ram revisited":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "path query data structures in practice")

Question: Please tell me which class the source paper 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)