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 "four dimensional dominance range reporting in linear space"):
  Abstract: In this paper we study the four-dimensional dominance range reporting problem and present data structures with linear or almost-linear space usage. Our results can be also used to answer four-dimensional queries that are bounded on five sides. The first data structure presented in this paper uses linear space and answers queries in $O(\log^{1+\varepsilon}n + k\log^{\varepsilon} n)$ time, where $k$ is the number of reported points, $n$ is the number of points in the data structure, and $\varepsilon$ is an arbitrarily small positive constant. Our second data structure uses $O(n \log^{\varepsilon} n)$ space and answers queries in $O(\log n+k)$ time. #R##N#These are the first data structures for this problem that use linear (resp. $O(n\log^{\varepsilon} n)$) space and answer queries in poly-logarithmic time. For comparison the fastest previously known linear-space or $O(n\log^{\varepsilon} n)$-space data structure supports queries in $O(n^{\varepsilon} + k)$ time (Bentley and Mauer, 1980). Our results can be generalized to $d\ge 4$ dimensions. For example, we can answer $d$-dimensional dominance range reporting queries in $O(\log\log n (\log n/\log\log n)^{d-3} + k)$ time using $O(n\log^{d-4+\varepsilon}n)$ space. Compared to the fastest previously known result (Chan, 2013), our data structure reduces the space usage by $O(\log n)$ without increasing the query time.

Includes members:
  Member "succinct indices for range queries with applications to orthogonal range maxima":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "range predecessor and lempel ziv parsing")
  Member "sorted range reporting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "path and ancestor queries on trees with multidimensional weight vectors")
  Member "more haste less waste lowering the redundancy in fully indexable dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nearly optimal static las vegas succinct dictionary")
  Member "space efficient multi dimensional range reporting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "orthogonal range reporting and rectangle stabbing for fat rectangles")

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)