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 "improved grammar based compressed indexes"):
  Abstract: We introduce the first grammar-compressed representation of a sequence that supports searches in time that depends only logarithmically on the size of the grammar. Given a text $T[1..u]$ that is represented by a (context-free) grammar of $n$ (terminal and nonterminal) symbols and size $N$ (measured as the sum of the lengths of the right hands of the rules), a basic grammar-based representation of $T$ takes $N\lg n$ bits of space. Our representation requires $2N\lg n + N\lg u + \epsilon\, n\lg n + o(N\lg n)$ bits of space, for any $0<\epsilon \le 1$. It can find the positions of the $occ$ occurrences of a pattern of length $m$ in $T$ in $O((m^2/\epsilon)\lg (\frac{\lg u}{\lg n}) +occ\lg n)$ time, and extract any substring of length $\ell$ of $T$ in time $O(\ell+h\lg(N/h))$, where $h$ is the height of the grammar tree.

Includes members:
  Member "orthogonal range searching on the ram revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "path query data structures in practice")
  Member "a faster grammar based self index":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "top tree compression of tries")
  Member "random access to grammar compressed strings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local decode and update for big data compression")

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)