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: "compressed data structures for dynamic sequences"
  Abstract: We consider the problem of storing a dynamic string $S$ over an alphabet $\Sigma=\{\,1,\ldots,\sigma\,\}$ in compressed form. Our representation supports insertions and deletions of symbols and answers three fundamental queries: $\mathrm{access}(i,S)$ returns the $i$-th symbol in $S$, $\mathrm{rank}_a(i,S)$ counts how many times a symbol $a$ occurs among the first $i$ positions in $S$, and $\mathrm{select}_a(i,S)$ finds the position where a symbol $a$ occurs for the $i$-th time. We present the first fully-dynamic data structure for arbitrarily large alphabets that achieves optimal query times for all three operations and supports updates with worst-case time guarantees. Ours is also the first fully-dynamic data structure that needs only $nH_k+o(n\log\sigma)$ bits, where $H_k$ is the $k$-th order entropy and $n$ is the string length. Moreover our representation supports extraction of a substring $S[i..i+\ell]$ in optimal $O(\log n/\log\log n + \ell/\log_{\sigma}n)$ time.

Connected through hyperedge HE-1 (induced by source paper "local decode and update for big data compression"):
  with members:
    - "how to store a random walk"
    - "set membership with non adaptive bit probes"
    - "set membership with a few bit probes"
    - "bounds on the parameters of locally recoverable codes"
    - "a family of optimal locally recoverable codes"
    - "update efficiency and local repairability limits for capacity approaching codes"
    - "on locally decodable source coding"
    - "a simple online competitive adaptation of lempel ziv compression with efficient random access support"

Connected through hyperedge HE-2 (induced by source paper "compressed dynamic range majority and minority data structures"):
  with members:
    - "optimal query time for encoding range majority"
    - "range majorities and minorities in arrays"
    - "compressed dynamic range majority data structures"
    - "optimal encodings for range majority queries"
    - "better space bounds for parameterized range majority and minority"
    - "optimal lower and upper bounds for representing sequences"
    - "dynamic range majority data structures"
    - "succinct representations of dynamic strings"

Connected through hyperedge HE-3 (induced by source paper "compressed dynamic range majority data structures"):
  with members:
    - "range majorities and minorities in arrays"
    - "dynamic range majority data structures"
    - "searching for frequent colors in rectangles"

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)