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: "range counting coresets for uncertain data"
  Abstract: We study coresets for various types of range counting queries on uncertain data. In our model each uncertain point has a probability density describing its location, sometimes defined as k distinct locations. Our goal is to construct a subset of the uncertain points, including their locational uncertainty, so that range counting queries can be answered by just examining this subset. We study three distinct types of queries. RE queries return the expected number of points in a query range. RC queries return the number of points in the range with probability at least a threshold. RQ queries returns the probability that fewer than some threshold fraction of the points are in the range. In both RC and RQ coresets the threshold is provided as part of the query. And for each type of query we provide coreset constructions with approximation-size tradeoffs. We show that random sampling can be used to construct each type of coreset, and we also provide significantly improved bounds using discrepancy-based approaches on axis-aligned range queries.

Connected through hyperedge HE-1 (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"
    - "convex hulls under uncertainty"
    - "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-2 (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"
    - "convex hulls under uncertainty"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"

Connected through hyperedge HE-3 (induced by source paper "range queries on uncertain data"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "a unified approach to ranking in probabilistic databases"

Connected through hyperedge HE-4 (induced by source paper "epsilon kernel coresets for stochastic points"):
  with members:
    - "range queries on uncertain data"
    - "convex hulls under uncertainty"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"
    - "flow computations on imprecise terrains"
    - "a unified framework for approximating and clustering data"
    - "on the expected complexity of random convex hulls"

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)