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 "compressed dynamic range majority and minority data structures"):
  Abstract: In the range $\alpha$-majority query problem, we are given a sequence $S[1..n]$ and a fixed threshold $\alpha \in (0, 1)$, and are asked to preprocess $S$ such that, given a query range $[i..j]$, we can efficiently report the symbols that occur more than $\alpha (j-i+1)$ times in $S[i..j]$, which are called the range $\alpha$-majorities. In this article we first describe a dynamic data structure that represents $S$ in compressed space --- $nH_k+ o(n\lg \sigma)$ bits for any $k = o(\log_{\sigma} n)$, where $\sigma$ is the alphabet size and $H_k \le H_0 \le \lg\sigma $ is the $k$-th order empirical entropy of $S$ --- and answers queries in $O \left(\frac{\log n}{\alpha \log \log n} \right)$ time while supporting insertions and deletions in $S$ in $O \left( \frac{\lg n}{\alpha} \right)$ amortized time. We then show how to modify our data structure to receive some $\beta \ge \alpha$ at query time and report the range $\beta$-majorities in $O \left( \frac{\log n}{\beta \log \log n} \right)$ time, without increasing the asymptotic space or update-time bounds. The best previous dynamic solution has the same query and update times as ours, but it occupies $O(n)$ words and cannot take advantage of being given a larger threshold $\beta$ at query time.  [ABSTRACT CLIPPED DUE TO LENGTH.]

Includes members:
  Member "optimal query time for encoding range majority":
    also connected through other hyperedges:
      (none)
  Member "range majorities and minorities in arrays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal query time for encoding range majority")
  Member "compressed dynamic range majority data structures":
    also connected through other hyperedges:
      (none)
  Member "compressed data structures for dynamic sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local decode and update for big data compression")
  Member "optimal encodings for range majority queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal query time for encoding range majority")
  Member "better space bounds for parameterized range majority and minority":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal query time for encoding range majority")
  Member "optimal lower and upper bounds for representing sequences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy consumption in compact integer vectors a study case")
  Member "dynamic range majority data structures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "range majorities and minorities in arrays")

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)