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 "optimal approximate polytope membership"):
  Abstract: In the polytope membership problem, a convex polytope K in ℝd is given, and the objective is to preprocess K into a data structure so that, given a query point q ∈ ℝd, it is possible to determine efficiently whether q ∈ K. We consider this problem in an approximate setting and assume that d is a constant. Given an approximation parameter e > 0, the query can be answered either way if the distance from q to K's boundary is at most e times K's diameter. Previous solutions to the problem were on the form of a space-time trade-off, where logarithmic query time demands O(1/ed−1) storage, whereas storage O(1/e(d−1)/2) admits roughly O(1/e(d−1)/8) query time. In this paper, we present a data structure that achieves logarithmic query time with storage of only O(1/e(d−1)/2), which matches the worst-case lower bound on the complexity of any e-approximating polytope. Our data structure is based on a new technique, a hierarchy of ellipsoids defined as approximations to Macbeath regions.   As an application, we obtain major improvements to approximate Euclidean nearest neighbor searching. Notably, the storage needed to answer e-approximate nearest neighbor queries for a set of n points in O(log n/e) time is reduced to O(n/ed/2). This halves the exponent in the e-dependency of the existing space bound of roughly O(n/ed), which has stood for 15 years (Har-Peled, 2001).

Includes members:
  Member "approximate polytope membership queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coresets for the nearest neighbor rule")
  Member "on the combinatorial complexity of approximating polytopes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal bound on the combinatorial complexity of approximating polytopes")
  Member "optimal detection of intersections between convex polyhedra":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reachability analysis of linear hybrid systems via block decomposition")
  Member "separation sensitive collision detection for convex objects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate polytope membership queries")

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)