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 "better space bounds for parameterized range majority and minority"):
  Abstract: Karpinski and Nekrich (2008) introduced the problem of parameterized range majority, which asks to preprocess a string of length $n$ such that, given the endpoints of a range, one can quickly find all the distinct elements whose relative frequencies in that range are more than a threshold $\tau$. Subsequent authors have reduced their time and space bounds such that, when $\tau$ is given at preprocessing time, we need either $\Oh{n \log (1 / \tau)}$ space and optimal $\Oh{1 / \tau}$ query time or linear space and $\Oh{(1 / \tau) \log \log \sigma}$ query time, where $\sigma$ is the alphabet size. In this paper we give the first linear-space solution with optimal $\Oh{1 / \tau}$ query time. For the case when $\tau$ is given at query time, we significantly improve previous bounds, achieving either $\Oh{n \log \log \sigma}$ space and optimal $\Oh{1 / \tau}$ query time or compressed space and $\Oh{(1 / \tau) \log \frac{\log (1 / \tau)}{\log w}}$ query time. Along the way, we consider the complementary problem of parameterized range minority that was recently introduced by Chan et al.\ (2012), who achieved linear space and $\Oh{1 / \tau}$ query time even for variable $\tau$. We improve their solution to use either nearly optimally compressed space with no slowdown, or optimally compressed space with nearly no slowdown. Some of our intermediate results, such as density-sensitive query time for one-dimensional range counting, may be of independent interest.

Includes members:
  Member "new lower and upper bounds for representing sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "run compressed rank select for large alphabets")
  Member "dynamic range majority data structures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed dynamic range majority and minority data structures")
  Member "efficient fully compressed sequence representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lyndon array construction during burrows wheeler inversion")
  Member "optimal succinctness for range minimum queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "space efficient construction of lyndon arrays in linear time")
  Member "searching for frequent colors in rectangles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed dynamic range majority and minority data structures")
  Member "range mode and range median queries on lists and trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "path query data structures in practice")
  Member "dynamic ordered sets with exponential search trees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved dynamic graph coloring")

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)