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: "nearest neighbor searching under uncertainty ii"
  Abstract: Nearest-neighbor search, which returns the nearest neighbor of a query point in a set of points, is an important and widely studied problem in many fields, and it has wide range of applications. In many of them, such as sensor databases, location-based services, face recognition, and mobile data, the location of data is imprecise. We therefore study nearest-neighbor queries in a probabilistic framework in which the location of each input point is specified as a probability distribution function. We present efficient algorithms for #R##N#- computing all points that are nearest neighbors of a query point with nonzero probability; and #R##N#- estimating the probability of a point being the nearest neighbor of a query point, either exactly or within a specified additive error.

Connected through hyperedge HE-1 (induced by source paper "updating dynamic random hyperbolic graphs in sublinear time"):
  with members:
    - "querying probabilistic neighborhoods in spatial data sets efficiently"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "generating scaled replicas of real world complex networks"
    - "sampling geometric inhomogeneous random graphs in linear time"
    - "geometric inhomogeneous random graphs"
    - "approximating betweenness centrality in fully dynamic networks"
    - "on the diameter of hyperbolic random graphs"
    - "fast generation of complex networks with underlying hyperbolic geometry"

Connected through hyperedge HE-2 (induced by source paper "on the expected diameter width and complexity of a stochastic convex hull"):
  with members:
    - "stochastic closest pair problem and most likely nearest neighbor search in tree spaces"
    - "colored stochastic dominance problems"
    - "on the separability of stochastic geometric objects with applications"
    - "epsilon kernel coresets for stochastic points"
    - "convex hulls under uncertainty"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"

Connected through hyperedge HE-3 (induced by source paper "covering uncertain points in a tree"):
  with members:
    - "stochastic k center and j flat center problems"
    - "convex hulls under uncertainty"

Connected through hyperedge HE-4 (induced by source paper "stochastic k center and j flat center problems"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "range queries on uncertain data"
    - "convex hulls under uncertainty"
    - "range counting coresets for uncertain data"
    - "on the sensitivity of shape fitting problems"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"
    - "a unified framework for approximating and clustering data"
    - "minimum enclosing polytope in high dimensions"

Connected through hyperedge HE-5 (induced by source paper "on the separability of stochastic geometric objects with applications"):
  with members:
    - "on the expected diameter width and complexity of a stochastic convex hull"
    - "stochastic closest pair problem and most likely nearest neighbor search in tree spaces"
    - "colored stochastic dominance problems"
    - "stochastic k center and j flat center problems"
    - "convex hulls under uncertainty"

Connected through hyperedge HE-6 (induced by source paper "stochastic closest pair problem and most likely nearest neighbor search in tree spaces"):
  with members:
    - "on the separability of stochastic geometric objects with applications"
    - "convex hulls under uncertainty"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"
    - "the geometric stability of voronoi diagrams with respect to small changes of the sites"

Connected through hyperedge HE-7 (induced by source paper "approximating the distribution of the median and other robust estimators on uncertain data"):
  with members:
    - "convex hulls under uncertainty"
    - "range counting coresets for uncertain data"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"

Connected through hyperedge HE-8 (induced by source paper "querying probabilistic neighborhoods in spatial data sets efficiently"):
  with members:
    - "geometric inhomogeneous random graphs"
    - "networkit a tool suite for large scale complex network analysis"

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)