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 "lempel ziv decoding in external memory"):
  Abstract: Simple and fast decoding is one of the main advantages of LZ77-type text encoding used in many popular file compressors such as gzip and 7zip. With the recent introduction of external memory algorithms for Lempel-Ziv factorization there is a need for external memory LZ77 decoding but the standard algorithm makes random accesses to the text and cannot be trivially modified for external memory computation. We describe the first external memory algorithms for LZ77 decoding, prove that their I/O complexity is optimal, and demonstrate that they are very fast in practice, only about three times slower than in-memory decoding (when reading input and writing output is included in the time).

Includes members:
  Member "hybrid indexes for repetitive datasets":
    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 "lempel ziv parsing in external memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rpair rescaling repair with rsync")
  Member "faster approximate pattern matching in compressed repetitive texts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine grained complexity of analyzing compressed data quantifying improvements over decompress and solve")
  Member "relative lempel ziv factorization for efficient storage and retrieval of web collections":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lempel ziv like parsing in small space")
  Member "grammar based compression in a streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constructing small tree grammars and small circuits for formulas")

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)