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: "faster compressed quadtrees"
  Abstract: Real-world point sets tend to be clustered, so using a machine word for each point is wasteful. In this paper we first bound the number of nodes in the quadtree for a point set in terms of the points' clustering. We then describe a quadtree data structure that uses $\mathcal{O} (1)$ bits per node and supports faster queries than previous structures with this property. Finally, we present experimental evidence that our structure is practical.

Connected through hyperedge HE-1 (induced by source paper "optimal joins using compact data structures"):
  with members:
    - "worst case optimal join algorithms techniques results and open problems"
    - "size bounds and query plans for relational joins"
    - "compressed representation of dynamic binary relations with applications"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"
    - "beyond worst case analysis for joins with minesweeper"

Connected through hyperedge HE-2 (induced by source paper "a compact representation of raster time series"):
  with members:
    - "towards a compact representation of temporal rasters"
    - "from theory to practice plug and play with succinct data structures"

Connected through hyperedge HE-3 (induced by source paper "aggregated 2d range queries on clustered points"):
  with members:
    - "compact binary relation representations with rich functionality"
    - "space efficient data analysis queries on grids"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-4 (induced by source paper "near optimal euclidean metric compression"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "succinct representation of well spaced point clouds"

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)