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 "optimal encodings for range majority queries"):
  Abstract: We study the problem of designing a data structure that reports the positions of the distinct $\tau$-majorities within any range of an array $A[1,n]$, without storing $A$. A $\tau$-majority in a range $A[i,j]$, for $0<\tau< 1$, is an element that occurs more than $\tau(j-i+1)$ times in $A[i,j]$. We show that $\Omega(n\log(1/\tau))$ bits are necessary for any data structure able just to count the number of distinct $\tau$-majorities in any range. Then, we design a structure using $O(n\log(1/\tau))$ bits that returns one position of each $\tau$-majority of $A[i,j]$ in $O((1/\tau)\log\log_w(1/\tau)\log n)$ time, on a RAM machine with word size $w$ (it can output any further position where each $\tau$-majority occurs in $O(1)$ additional time). Finally, we show how to remove a $\log n$ factor from the time by adding $O(n\log\log n)$ bits of space to the structure.

Includes members:
  Member "better space bounds for parameterized range majority and minority":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed dynamic range majority and minority data structures")
  Member "top k color queries for document retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate range counting revisited")
  Member "searching for frequent colors in rectangles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed dynamic range majority and minority data structures")
  Member "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "path query data structures in practice")
  Member "practical entropy compressed rank select dictionary":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bitvectors with runs and the successor predecessor problem")
  Member "time space trade offs for predecessor search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient and compact representations of prefix codes")

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)