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 "rpair rescaling repair with rsync"):
  Abstract: Data compression is a powerful tool for managing massive but repetitive datasets, especially schemes such as grammar-based compression that support computation over the data without decompressing it. In the best case such a scheme takes a dataset so big that it must be stored on disk and shrinks it enough that it can be stored and processed in internal memory. Even then, however, the scheme is essentially useless unless it can be built on the original dataset reasonably quickly while keeping the dataset on disk. In this paper we show how we can preprocess such datasets with context-triggered piecewise hashing such that afterwards we can apply RePair and other grammar-based compressors more easily. We first give our algorithm, then show how a variant of it can be used to approximate the LZ77 parse, then leverage that to prove theoretical bounds on compression, and finally give experimental evidence that our approach is competitive in practice.

Includes members:
  Member "gract a grammar based compressed index for trajectory data":
    also connected through other hyperedges:
      (none)
  Member "mr repair grammar compression based on maximal repeats":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grammar compression with probabilistic context free grammar")
  Member "prefix free parsing for building big bwts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space")
  Member "time space trade offs for lempel ziv compressed indexing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "top tree compression of tries")
  Member "compressed indexing with signature grammars":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "top tree compression of tries")
  Member "universal indexes for highly repetitive document collections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the reproducibility of experiments of indexing repetitive document collections")
  Member "dynamic index lz factorization and 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 "a really simple approximation of smallest grammar":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards a definitive measure of repetitiveness")

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)