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: "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.

Connected through hyperedge HE-1 (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"
    - "compressed data structures for dynamic sequences"
    - "optimal encodings for range majority queries"
    - "optimal lower and upper bounds for representing sequences"
    - "dynamic range majority data structures"
    - "succinct representations of dynamic strings"

Connected through hyperedge HE-2 (induced by source paper "optimal query time for encoding range majority"):
  with members:
    - "range majorities and minorities in arrays"
    - "optimal encodings for range majority queries"
    - "searching for frequent colors in rectangles"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-3 (induced by source paper "optimal encodings for range majority queries"):
  with members:
    - "top k color queries for document retrieval"
    - "searching for frequent colors in rectangles"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "practical entropy compressed rank select dictionary"
    - "time space trade offs for predecessor search"

Connected through hyperedge HE-4 (induced by source paper "new algorithms for position heaps"):
  with members:
    - "efficient lz78 factorization of grammar compressed text"
    - "on line construction of position heaps"
    - "efficient fully compressed sequence representations"

Connected through hyperedge HE-5 (induced by source paper "dynamic range majority data structures"):
  with members:
    - "optimal dynamic sequence representations"
    - "space efficient data analysis queries on grids"
    - "dynamic range selection in linear space"
    - "counting colours in compressed strings"
    - "searching for frequent colors in rectangles"
    - "dynamic ordered sets with exponential search trees"

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)