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 "a really simple approximation of smallest grammar"):
  Abstract: In this paper we present a really simple linear-time algorithm constructing a context-free grammar of size O(g log (N/g)) for the input string, where N is the size of the input string and g the size of the optimal grammar generating this string. The algorithm works for arbitrary size alphabets, but the running time is linear assuming that the alphabet Sigma of the input string can be identified with numbers from 1,ldots, N^c for some constant c. Algorithms with such an approximation guarantee and running time are known, however all of them were non-trivial and their analyses were involved. The here presented algorithm computes the LZ77 factorisation and transforms it in phases to a grammar. In each phase it maintains an LZ77-like factorisation of the word with at most l factors as well as additional O(l) letters, where l was the size of the original LZ77 factorisation. In one phase in a greedy way (by a left-to-right sweep and a help of the factorisation) we choose a set of pairs of consecutive letters to be replaced with new symbols, i.e. nonterminals of the constructed grammar. We choose at least 2/3 of the letters in the word and there are O(l) many different pairs among them. Hence there are O(log N) phases, each of them introduces O(l) nonterminals to a grammar. A more precise analysis yields a bound O(l log(N/l)). As l \leq g, this yields the desired bound O(g log(N/g)).

Includes members:
  Member "approximation of grammar based compression via recompression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on string constraint solving")
  Member "approximation of smallest linear tree grammar":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "repair in compressed space and time")
  Member "linear time lempel ziv factorization simple fast small":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lempel ziv like parsing in small space")
  Member "simpler and faster lempel ziv factorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bidirectional text compression in external memory")
  Member "pattern matching in lempel ziv compressed strings fast simple and deterministic":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal time dictionary compressed indexes")
  Member "faster fully compressed pattern matching by recompression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grammar compression with probabilistic context free grammar")
  Member "the similarity metric":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "max margin class imbalanced learning with gaussian affinity")
  Member "identifying hierarchical structure in sequences a linear time algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way")

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)