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: "towards optimal range medians"
  Abstract: We consider the following problem: given an unsorted array of $n$ elements, and a sequence of intervals in the array, compute the median in each of the subarrays defined by the intervals. We describe a simple algorithm which uses O(n) space and needs $O(n\log k + k\log n)$ time to answer the first $k$ queries. This improves previous algorithms by a logarithmic factor and matches a lower bound for $k=O(n)$. #R##N#Since the algorithm decomposes the range of element values rather than the array, it has natural generalizations to higher dimensional problems -- it reduces a range median query to a logarithmic number of range counting queries.

Connected through hyperedge HE-1 (induced by source paper "on approximate range mode and range selection"):
  with members:
    - "improved time and space bounds for dynamic range mode"
    - "dynamic range selection in linear space"
    - "range quantile queries another virtue of wavelet trees"
    - "range mode and range median queries on lists and trees"

Connected through hyperedge HE-2 (induced by source paper "linear space data structures for range mode query in arrays"):
  with members:
    - "range quantile queries another virtue of wavelet trees"
    - "biased range trees"
    - "optimal succinctness for range minimum queries"
    - "range mode and range median queries on lists and trees"

Connected through hyperedge HE-3 (induced by source paper "dynamic range selection in linear space"):
  with members:
    - "the cell probe complexity of dynamic range counting"
    - "succinct representations of dynamic strings"
    - "range quantile queries another virtue of wavelet trees"
    - "range mode and range median queries on lists and trees"

Connected through hyperedge HE-4 (induced by source paper "new algorithms on wavelet trees and applications to information retrieval"):
  with members:
    - "compressed representations of permutations and applications"
    - "range quantile queries another virtue of wavelet trees"
    - "fast in memory xpath search over compressed text and tree indexes"
    - "practical entropy compressed rank select dictionary"
    - "range mode and range median queries on lists and trees"

Connected through hyperedge HE-5 (induced by source paper "range quantile queries another virtue of wavelet trees"):
  with members:
    - "range mode and range median queries on lists and 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)