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: "biased range trees"
  Abstract: A data structure, called a biased range tree, is presented that preprocesses a set S of n points in R^2 and a query distribution D for 2-sided orthogonal range counting queries. The expected query time for this data structure, when queries are drawn according to D, matches, to within a constant factor, that of the optimal decision tree for S and D. The memory and preprocessing requirements of the data structure are O(n log n).

Connected through hyperedge HE-1 (induced by source paper "instance optimal geometric algorithms"):
  with members:
    - "entropy triangulation and point location in planar subdivisions"
    - "self improving algorithms"
    - "odds on trees"
    - "optimal adaptive algorithms for finding the nearest and farthest point on a parametric black box curve"
    - "optimal aggregation algorithms for middleware"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"
    - "dense point sets have sparse delaunay triangulations"

Connected through hyperedge HE-2 (induced by source paper "linear space data structures for range mode query in arrays"):
  with members:
    - "range quantile queries another virtue of wavelet trees"
    - "towards optimal range medians"
    - "optimal succinctness for range minimum queries"
    - "range mode and range median queries on lists and trees"

Connected through hyperedge HE-3 (induced by source paper "point location in disconnected planar subdivisions"):
  with members:
    - "entropy triangulation and point location in planar subdivisions"

Connected through hyperedge HE-4 (induced by source paper "odds on trees"):
  with members:
    - "entropy triangulation and point location in planar subdivisions"

Connected through hyperedge HE-5 (induced by source paper "priority range trees"):
  with members:
    - "instance optimal geometric algorithms"

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.CG(Computational Geometry)