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 like parsing in small space"):
  Abstract: Lempel-Ziv (LZ77 or, briefly, LZ) is one of the most effective and widely-used compressors for repetitive texts. However, the existing efficient methods computing the exact LZ parsing have to use linear or close to linear space to index the input text during the construction of the parsing, which is prohibitive for long inputs. An alternative is Relative Lempel-Ziv (RLZ), which indexes only a fixed reference sequence, whose size can be controlled. Deriving the reference sequence by sampling the text yields reasonable compression ratios for RLZ, but performance is not always competitive with that of LZ and depends heavily on the similarity of the reference to the text. In this paper we introduce ReLZ, a technique that uses RLZ as a preprocessor to approximate the LZ parsing using little memory. RLZ is first used to produce a sequence of phrases, and these are regarded as metasymbols that are input to LZ for a second-level parsing on a (most often) drastically shorter sequence. This parsing is finally translated into one on the original sequence. #R##N#We analyze the new scheme and prove that, like LZ, it achieves the $k$th order empirical entropy compression $n H_k + o(n\log\sigma)$ with $k = o(\log_\sigma n)$, where $n$ is the input length and $\sigma$ is the alphabet size. In fact, we prove this entropy bound not only for ReLZ but for a wide class of LZ-like encodings. Then, we establish a lower bound on ReLZ approximation ratio showing that the number of phrases in it can be $\Omega(\log n)$ times larger than the number of phrases in LZ. Our experiments show that ReLZ is orders of magnitude faster than other alternatives to compute the (exact or approximate) LZ parsing, at the reasonable price of an approximation factor below $2.0$ in practice, and sometimes below $1.05$, to the size of LZ.

Includes members:
  Member "at the roots of dictionary compression string attractors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards a definitive measure of repetitiveness")
  Member "relations between greedy and bit optimal lz77 encodings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "comparison of lz77 type parsings")
  Member "lz end parsing in compressed space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bidirectional text compression in external memory")
  Member "approximating lz77 via small space multiple pattern matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time space tradeoffs for finding a long common substring")
  Member "from theory to practice plug and play with succinct data structures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bitvectors with runs and the successor predecessor problem")
  Member "lempel ziv parsing in external memory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rpair rescaling repair with rsync")
  Member "linear time lempel ziv factorization simple fast small":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space")
  Member "relative lempel ziv factorization for efficient storage and retrieval of web collections":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rlzap relative lempel ziv with adaptive pointers")

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)