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: "aggregated 2d range queries on clustered points"
  Abstract: Efficient processing of aggregated range queries on two-dimensional grids is a common requirement in information retrieval and data mining systems, for example in Geographic Information Systems and OLAP cubes. We introduce a technique to represent grids supporting aggregated range queries that requires little space when the data points in the grid are clustered, which is common in practice. We show how this general technique can be used to support two important types of aggregated queries, which are ranked range queries and counting range queries. Our experimental evaluation shows that this technique can speed up aggregated queries up to more than an order of magnitude, with a small space overhead. HighlightsSpace-efficient representation for two-dimensional grids.Efficient support for aggregated range queries.Proved performance in main memory.Results competitive with the state of the art.Applications to several domains: Geographic Information Systems, OLAP cubes, etc.

Connected through hyperedge HE-1 (induced by source paper "path query data structures in practice"):
  with members:
    - "orthogonal range searching on the ram revisited"
    - "succinct representations of permutations and functions"
    - "range quantile queries another virtue of wavelet trees"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "range mode and range median queries on lists and trees"

Connected through hyperedge HE-2 (induced by source paper "extending general compact querieable representations to gis applications"):
  with members:
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "orthogonal range searching on the ram revisited"

Connected through hyperedge HE-3 (induced by source paper "efficient processing of raster and vector data"):
  with members:
    - "a review of eo image information mining"

Connected through hyperedge HE-4 (induced by source paper "faster dynamic compressed d ary relations"):
  with members:
    - "compact and efficient representation of general graph databases"
    - "towards a compact representation of temporal rasters"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "fully functional static and dynamic succinct trees"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-5 (induced by source paper "about birds project bioinformatics and information retrieval data structures analysis and design"):
  with members:
    - "compact representations of event sequences"
    - "parallel construction of wavelet trees on multicore architectures"
    - "run compressed rank select for large alphabets"
    - "efficient compression and indexing of trajectories"
    - "grammar compressed sequences with rank select support"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "navigational rule derivation an algorithm to determine the effect of traffic signs on road networks"

Connected through hyperedge HE-6 (induced by source paper "parallel range and segment queries with augmented maps"):
  with members:
    - "pam parallel augmented maps"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "priority range 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)