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: "encoding 2 d range maximum queries"
  Abstract: We consider the \emph{two-dimensional range maximum query (2D-RMQ)} problem: given an array $A$ of ordered values, to pre-process it so that we can find the position of the smallest element in the sub-matrix defined by a (user-specified) range of rows and range of columns. We focus on determining the \emph{effective} entropy of 2D-RMQ, i.e., how many bits are needed to encode $A$ so that 2D-RMQ queries can be answered \emph{without} access to $A$. We give tight upper and lower bounds on the expected effective entropy for the case when $A$ contains independent identically-distributed random values, and new upper and lower bounds for arbitrary $A$, for the case when $A$ contains few rows. The latter results improve upon previous upper and lower bounds by Brodal et al. (ESA 2010). In some cases we also give data structures whose space usage is close to the effective entropy and answer 2D-RMQ queries rapidly.

Connected through hyperedge HE-1 (induced by source paper "simultaneous encodings for range and next previous larger smaller value queries"):
  with members:
    - "optimal encodings for range top k selection and min max"
    - "combined data structure for previous and next smaller values"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-2 (induced by source paper "orthogonal range searching for text indexing"):
  with members:
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "binary jumbled string matching for highly run length compressible texts"
    - "linear space substring range counting over polylogarithmic alphabets"
    - "compact binary relation representations with rich functionality"
    - "on optimal top k string retrieval"
    - "sorted range reporting"
    - "orthogonal range searching on the ram revisited"
    - "improved grammar based compressed indexes"

Connected through hyperedge HE-3 (induced by source paper "a simple linear space data structure for constant time range minimum query"):
  with members:
    - "optimal succinctness for range minimum queries"
    - "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)