human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "self improving algorithms for coordinate wise maxima and convex hulls"):
  Abstract: Finding the coordinate-wise maxima and the convex hull of a planar point set are probably the most classic problems in computational geometry. We consider these problems in the selfimproving setting. Here, we have n distributionsD1;:::;Dn of planar points. An input point set (p1;:::;pn) is generated by taking an independent sample pi from each Di, so the input is distributed according to the product D = Q i Di. A self-improving algorithm repeatedly gets inputs from the distribution D (which is a priori unknown), and it tries to optimize its running time for D. The algorithm uses the rst few inputs to learn salient features of the distributionD, before it becomes ne-tuned to D. Let OPT-MAXD (resp. OPT-CHD) be the expected depth of an optimal linear comparison tree computing the maxima (resp. convex hull) forD. Our maxima algorithm eventually achieves expected running time O(OPT-MAXD +n). Furthermore, we give a self-improving algorithm for convex hulls with expected running time O(OPT-CHD +n log logn). Our results require new tools for understanding linear comparison trees. In particular, we convert a general linear comparison tree to a restricted version that can then be related to the running time of our algorithms. Another interesting feature is an interleaved search procedure to determine the likeliest point to be extremal with minimal computation. This allows our algorithms to be competitive with the optimal algorithm forD.

Includes members:
  Member "instance optimal geometric algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic distribution sensitive point location")
  Member "convex hull of points lying on lines in o nlogn time after preprocessing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a framework for robust realistic geometric computations")
  Member "convex hull of imprecise points in o n log n time after preprocessing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sharp bounds on davenport schinzel sequences of every order")
  Member "self improving algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learned frequency estimation algorithms under zipfian distribution")
  Member "odds on trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic distribution sensitive point location")

Question: Please tell me which class the source paper 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)