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: "odds on trees"
  Abstract: Let R^d -> A be a query problem over R^d for which there exists a data structure S that can compute P(q) in O(log n) time for any query point q in R^d. Let D be a probability measure over R^d representing a distribution of queries. We describe a data structure called the odds-on tree, of size O(n^\epsilon) that can be used as a filter that quickly computes P(q) for some query values q in R^d and relies on S for the remaining queries. With an odds-on tree, the expected query time for a point drawn according to D is O(H*+1), where H* is a lower-bound on the expected cost of any linear decision tree that solves P. #R##N#Odds-on trees have a number of applications, including distribution-sensitive data structures for point location in 2-d, point-in-polytope testing in d dimensions, ray shooting in simple polygons, ray shooting in polytopes, nearest-neighbour queries in R^d, point-location in arrangements of hyperplanes in R^d, and many other geometric searching problems that can be solved in the linear-decision tree model. A standard lifting technique extends these results to algebraic decision trees of constant degree. A slightly different version of odds-on trees yields similar results for orthogonal searching problems that can be solved in the comparison tree model.

Connected through hyperedge HE-1 (induced by source paper "dynamic distribution sensitive point location"):
  with members:
    - "point location in incremental planar subdivisions"
    - "point location in dynamic planar subdivisions"
    - "adaptive planar point location"
    - "instance optimal geometric algorithms"
    - "entropy triangulation and point location in planar subdivisions"
    - "a static optimality transformation with applications to planar point location"

Connected through hyperedge HE-2 (induced by source paper "instance optimal geometric algorithms"):
  with members:
    - "entropy triangulation and point location in planar subdivisions"
    - "self improving algorithms"
    - "biased range 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-3 (induced by source paper "a static optimality transformation with applications to planar point location"):
  with members:
    - "entropy triangulation and point location in planar subdivisions"
    - "improved upper bounds for pairing heaps"
    - "self improving algorithms"
    - "point location in disconnected planar subdivisions"
    - "layered working set trees"

Connected through hyperedge HE-4 (induced by source paper "self improving algorithms for coordinate wise maxima and convex hulls"):
  with members:
    - "instance optimal geometric algorithms"
    - "convex hull of points lying on lines in o nlogn time after preprocessing"
    - "convex hull of imprecise points in o n log n time after preprocessing"
    - "self improving 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)