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: "stochastic k center and j flat center problems"
  Abstract: Solving geometric optimization problems over uncertain data have become increasingly important in many applications and have attracted a lot of attentions in recent years. In this paper, we study two important geometric optimization problems, the $k$-center problem and the $j$-flat-center problem, over stochastic/uncertain data points in Euclidean spaces. For the stochastic $k$-center problem, we would like to find $k$ points in a fixed dimensional Euclidean space, such that the expected value of the $k$-center objective is minimized. For the stochastic $j$-flat-center problem, we seek a $j$-flat (i.e., a $j$-dimensional affine subspace) such that the expected value of the maximum distance from any point to the $j$-flat is minimized. We consider both problems under two popular stochastic geometric models, the existential uncertainty model, where the existence of each point may be uncertain, and the locational uncertainty model, where the location of each point may be uncertain. We provide the first PTAS (Polynomial Time Approximation Scheme) for both problems under the two models. Our results generalize the previous results for stochastic minimum enclosing ball and stochastic enclosing cylinder.

Connected through hyperedge HE-1 (induced by source paper "probabilistic smallest enclosing ball in high dimensions via subgradient sampling"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "geometric median in nearly linear time"

Connected through hyperedge HE-2 (induced by source paper "approximation algorithms for stochastic clustering"):
  with members:
    - "fair clustering through fairlets"
    - "improvements on the k center problem for uncertain data"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "a lottery model for center type problems with outliers"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"

Connected through hyperedge HE-3 (induced by source paper "covering uncertain points in a tree"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "convex hulls under uncertainty"

Connected through hyperedge HE-4 (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"
    - "convex hulls under uncertainty"

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)