human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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 $g$.

Connected through hyperedge HE-1 (induced by source paper "lempel ziv decoding in external memory"):
  with members:
    - "hybrid indexes for repetitive datasets"
    - "lempel ziv parsing in external memory"
    - "faster approximate pattern matching in compressed repetitive texts"
    - "relative lempel ziv factorization for efficient storage and retrieval of web collections"

Connected through hyperedge HE-2 (induced by source paper "constructing small tree grammars and small circuits for formulas"):
  with members:
    - "traversing grammar compressed trees with constant delay"
    - "tree compression with top trees revisited"
    - "a really simple approximation of smallest grammar"
    - "a universal grammar based code for lossless compression of binary trees"
    - "tree compression with top trees"
    - "approximation of grammar based compression via recompression"
    - "approximation of smallest linear tree grammar"

Connected through hyperedge HE-3 (induced by source paper "lempel ziv parsing in external memory"):
  with members:
    - "lightweight lempel ziv parsing"
    - "linear time lempel ziv factorization simple fast small"
    - "new lower and upper bounds for representing sequences"
    - "lightweight data indexing and compression in external memory"

Connected through hyperedge HE-4 (induced by source paper "on the value of multiple read write streams for data compression"):
  with members:
    - "lightweight data indexing and compression in external memory"
    - "reversal complexity revisited"
    - "large alphabets and incompressibility"
    - "tight lower bounds for query processing on streaming and external memory data"

Connected through hyperedge HE-5 (induced by source paper "faster approximate pattern matching in compressed repetitive texts"):
  with members:
    - "random access to grammar compressed strings"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "practical entropy compressed rank select dictionary"

Question: Please tell me which class the center node 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)