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 "entropy bounds for grammar compression"):
  Abstract: In grammar compression we represent a string as a context free grammar. This model is popular both in theoretical and practical applications due to its simplicity, good compression rate and suitability for processing of the compressed representations. In practice, achieving compression requires encoding such grammar as a binary string, there are a few commonly used. We bound the size of such encodings for several compression methods, along with well-known \RePair algorithm. For \RePair we prove that its standard encoding, which is a combination of entropy coding and special encoding of a grammar, achieves $1.5|S|H_k(S)$. We also show that by stopping after some iteration we can achieve $|S|H_k(S)$. The latter is particularly important, as it explains the phenomenon observed in practice, that introducing too many nonterminals causes the bit-size to grow. We generalize our approach to other compressions methods like \Greedy or wide class of irreducible grammars, and other bit encodings (including naive, which uses fixed-length codes). Our approach not only proves the bounds but also partially explains why \Greedy and \RePair are much better in practice than the other grammar based methods. At last, we show that for a wide family of dictionary compression methods (including grammar compressors) $\Omega\left(nk \log \sigma/\log_\sigma n\right)$ bits of redundancy are required. This shows a separation between context-based/BWT methods and dictionary compression algorithms, as for the former there exists methods where redundancy does not depend on $n$, but only on $k$~and~$\sigma$.

Includes members:
  Member "approximation ratio of repair":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "repair in compressed space and time")
  Member "a really simple approximation of smallest grammar":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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)