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: "on optimally partitioning a text to improve its compression"
  Abstract: In this paper we investigate the problem of partitioning an input string T in such a way that compressing individually its parts via a base-compressor C gets a compressed output that is shorter than applying C over the entire T at once. This problem was introduced in the context of table compression, and then further elaborated and extended to strings and trees. Unfortunately, the literature offers poor solutions: namely, we know either a cubic-time algorithm for computing the optimal partition based on dynamic programming, or few heuristics that do not guarantee any bounds on the efficacy of their computed partition, or algorithms that are efficient but work in some specific scenarios (such as the Burrows-Wheeler Transform) and achieve compression performance that might be worse than the optimal-partitioning by a $\Omega(\sqrt{\log n})$ factor. Therefore, computing efficiently the optimal solution is still open. In this paper we provide the first algorithm which is guaranteed to compute in $O(n \log_{1+\eps}n)$ time a partition of T whose compressed output is guaranteed to be no more than $(1+\epsilon)$-worse the optimal one, where $\epsilon$ may be any positive constant.

Connected through hyperedge HE-1 (induced by source paper "on optimally partitioning variable byte codes"):
  with members:
    - "vectorized vbyte decoding"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "improving table compression with combinatorial optimization"

Connected through hyperedge HE-2 (induced by source paper "the alternating bwt an algorithmic perspective"):
  with members:
    - "a new class of searchable and provably highly compressible string transformations"
    - "on generalized lyndon words"
    - "external memory bwt and lcp computation for sequence collections with applications"
    - "lightweight lcp construction for very large collections of strings"
    - "large scale compression of genomic sequence databases with the burrows wheeler transform"
    - "optimal lower and upper bounds for representing sequences"
    - "a note on the burrows wheeler transformation"

Connected through hyperedge HE-3 (induced by source paper "variable byte encoding is now space efficient too"):
  with members:
    - "stream vbyte faster byte oriented integer compression"
    - "universal indexes for highly repetitive document collections"
    - "vectorized vbyte decoding"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "quasi succinct indices"
    - "decoding billions of integers per second through vectorization"
    - "improving table compression with combinatorial optimization"

Connected through hyperedge HE-4 (induced by source paper "bicriteria data compression"):
  with members:
    - "bicriteria network design problems"

Connected through hyperedge HE-5 (induced by source paper "new algorithms and lower bounds for sequential access data compression"):
  with members:
    - "low memory adaptive prefix coding"
    - "dynamic asymmetric communication"
    - "reversal complexity revisited"
    - "large alphabets and incompressibility"
    - "tight lower bounds for query processing on streaming and external memory data"
    - "dynamic shannon coding"

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)