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 "at the roots of dictionary compression string attractors"):
  Abstract: A well-known fact in the field of lossless text compression is that high-order entropy is a weak model when the input contains long repetitions. Motivated by this fact, decades of research have generated myriads of so-called dictionary compressors: algorithms able to reduce the text's size by exploiting its repetitiveness. Lempel-Ziv 77 is probably one of the most successful and known tools of this kind, followed by straight-line programs, run-length Burrows-Wheeler transform, macro schemes, collage systems, and the compact directed acyclic word graph. In this paper, we show that these techniques are only different solutions to the same, elegant, combinatorial problem: to find a small set of positions capturing all distinct text's substrings. We call \emph{string attractor} such a set. We first show reductions between dictionary compressors and string attractors. This gives us the approximation ratios of dictionary compressors with respect to the smallest string attractor and allows us to solve several open problems related to the asymptotic relations between the output sizes of different dictionary compressors. We then show that $k$-attractor problem --- that is, deciding whether a text has a size-$t$ set of positions capturing all substrings of length at most $k$ --- is NP-complete for $k\geq 3$. This, in particular, implies the NP-completeness of the full string attractor problem. We provide several approximation techniques for the smallest $k$-attractor, show that the problem is APX-complete for constant $k$, and give strong inapproximability results. To conclude, we provide matching lower- and upper- bounds for the random access problem on string attractors. Our data structure matching the lower bound is optimal also for LZ77, straight-line programs, collage systems, and macro schemes, and therefore essentially closes the random access problem for all these compressors.

Includes members:
  Member "representing the suffix tree with the cdawg":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "time space trade offs for lempel ziv compressed indexing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "top tree compression of tries")
  Member "fast label extraction in the cdawg":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "optimal time text indexing in bwt runs bounded space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast indexes for gapped pattern matching")
  Member "space efficient construction of compressed indexes in deterministic linear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering")
  Member "fully dynamic data structure for lce queries in compressed space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space")
  Member "composite repetition aware data structures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on extensions of maximal repeats in compressed strings")
  Member "linear time construction of compressed text indices in compact space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering")

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)