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: "convex hulls under uncertainty"
  Abstract: We study the convex-hull problem in a probabilistic setting, motivated by the need to handle data uncertainty inherent in many applications, including sensor databases, location-based services and computer vision. In our framework, the uncertainty of each input site is described by a probability distribution over a finite number of possible locations including a \emph{null} location to account for non-existence of the point. Our results include both exact and approximation algorithms for computing the probability of a query point lying inside the convex hull of the input, time-space tradeoffs for the membership queries, a connection between Tukey depth and membership queries, as well as a new notion of $\some$-hull that may be a useful representation of uncertain hulls.

Connected through hyperedge HE-1 (induced by source paper "computing shapley values for mean width in 3 d"):
  with members:
    - "computing shapley values in the plane"
    - "on the expected diameter width and complexity of a stochastic convex hull"
    - "epsilon kernel coresets for stochastic points"
    - "batched point location in sinr diagrams via algebraic tools"

Connected through hyperedge HE-2 (induced by source paper "computing shapley values in the plane"):
  with members:
    - "on the separability of stochastic geometric objects with applications"
    - "batched point location in sinr diagrams via algebraic tools"
    - "area and perimeter of the convex hull of stochastic points"
    - "computing the distance between piecewise linear bivariate functions"

Connected through hyperedge HE-3 (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"
    - "nearest neighbor searching under uncertainty ii"
    - "on the separability of stochastic geometric objects with applications"
    - "epsilon kernel coresets for stochastic points"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"

Connected through hyperedge HE-4 (induced by source paper "covering uncertain points in a tree"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "stochastic k center and j flat center problems"

Connected through hyperedge HE-5 (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"
    - "nearest neighbor searching under uncertainty ii"
    - "range queries on uncertain data"
    - "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-6 (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"
    - "nearest neighbor searching under uncertainty ii"
    - "stochastic k center and j flat center problems"

Connected through hyperedge HE-7 (induced by source paper "stochastic closest pair problem and most likely nearest neighbor search in tree spaces"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "on the separability of stochastic geometric objects with applications"
    - "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-8 (induced by source paper "approximating the distribution of the median and other robust estimators on uncertain data"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "range counting coresets for uncertain data"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"

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)