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 "grammar based compression in a streaming model"):
  Abstract: We show that, given a string $s$ of length $n$, with constant memory and logarithmic passes over a constant number of streams we can build a context-free grammar that generates $s$ and only $s$ and whose size is within an $\Oh{\min (g \log g, \sqrt{n \log g})}$-factor of the minimum $g$. This stands in contrast to our previous result that, with polylogarithmic memory and polylogarithmic passes over a single stream, we cannot build such a grammar whose size is within any polynomial of $g$.

Includes members:
  Member "on the value of multiple read write streams for data compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unidirectional input output streaming complexity of reversal and sorting")
  Member "random access to grammar compressed strings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local decode and update for big data compression")
  Member "recognizing well parenthesized expressions in the streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory augmented recurrent neural networks can learn generalized dyck languages")
  Member "lightweight data indexing and compression in external memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "external memory bwt and lcp computation for sequence collections with applications")
  Member "reversal complexity revisited":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unidirectional input output streaming complexity of reversal and sorting")

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)