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: "dynamic range majority data structures"
  Abstract: Given a set $P$ of coloured points on the real line, we study the problem of answering range $\alpha$-majority (or "heavy hitter") queries on $P$. More specifically, for a query range $Q$, we want to return each colour that is assigned to more than an $\alpha$-fraction of the points contained in $Q$. We present a new data structure for answering range $\alpha$-majority queries on a dynamic set of points, where $\alpha \in (0,1)$. Our data structure uses O(n) space, supports queries in $O((\lg n) / \alpha)$ time, and updates in $O((\lg n) / \alpha)$ amortized time. If the coordinates of the points are integers, then the query time can be improved to $O(\lg n / (\alpha \lg \lg n) + (\lg(1/\alpha))/\alpha))$. For constant values of $\alpha$, this improved query time matches an existing lower bound, for any data structure with polylogarithmic update time. We also generalize our data structure to handle sets of points in d-dimensions, for $d \ge 2$, as well as dynamic arrays, in which each entry is a colour.

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"
    - "better space bounds for parameterized range majority and minority"
    - "optimal lower and upper bounds for representing sequences"
    - "succinct representations of dynamic strings"

Connected through hyperedge HE-2 (induced by source paper "range majorities and minorities in arrays"):
  with members:
    - "optimal encodings for range majority queries"
    - "optimal lower and upper bounds for representing sequences"
    - "more haste less waste lowering the redundancy in fully indexable dictionaries"
    - "optimal succinctness for range minimum queries"
    - "searching for frequent colors in rectangles"
    - "range mode and range median queries on lists and trees"

Connected through hyperedge HE-3 (induced by source paper "compressed dynamic range majority data structures"):
  with members:
    - "range majorities and minorities in arrays"
    - "compressed data structures for dynamic sequences"
    - "searching for frequent colors in rectangles"

Connected through hyperedge HE-4 (induced by source paper "memory adjustable navigation piles with applications to sorting and convex hulls"):
  with members:
    - "space efficient plane sweep algorithms"
    - "selection from read only memory with limited workspace"
    - "memory constrained algorithms for simple polygons"
    - "convex hull and linear programming in read only setup with limited work space"
    - "space time trade offs for stack based algorithms"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-5 (induced by source paper "better space bounds for parameterized range majority and minority"):
  with members:
    - "new lower and upper bounds for representing sequences"
    - "efficient fully compressed sequence representations"
    - "optimal succinctness for range minimum queries"
    - "searching for frequent colors in rectangles"
    - "range mode and range median queries on lists and trees"
    - "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)