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: "deterministic sampling and range counting in geometric data streams"
  Abstract: We present memory-efficient deterministic algorithms for constructing e-nets and e-approximations of streams of geometric data. Unlike probabilistic approaches, these deterministic samples provide guaranteed bounds on their approximation factors. We show how our deterministic samples can be used to answer approximate online iceberg geometric queries on data streams. We use these techniques to approximate several robust statistics of geometric data streams, including Tukey depth, simplicial depth, regression depth, the Thiel-Sen estimator, and the least median of squares. Our algorithms use only a polylogarithmic amount of memory, provided the desired approximation factors are at least inverse-polylogarithmic. We also include a lower bound for noniceberg geometric queries.

Connected through hyperedge HE-1 (induced by source paper "the adversarial robustness of sampling"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "introduction to online convex optimization"
    - "weighted reservoir sampling from distributed streams"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic bandits robust to adversarial corruptions"
    - "the everlasting database statistical validity at a fair price"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "adaptive online learning in dynamic environments"

Connected through hyperedge HE-2 (induced by source paper "on the streaming complexity of fundamental geometric problems"):
  with members:
    - "optimal quantile approximation in streams"
    - "communication complexity for algorithm designers"
    - "interval selection in the streaming model"
    - "space constrained interval selection"

Connected through hyperedge HE-3 (induced by source paper "clustering high dimensional dynamic data streams"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "heavy hitters via cluster preserving clustering"
    - "a unified framework for approximating and clustering data"
    - "k median clustering model based compressive sensing and sparse recovery for earth mover distance"

Connected through hyperedge HE-4 (induced by source paper "coresets and sketches"):
  with members:
    - "coresets for k means and k median clustering and their applications"
    - "optimal quantile approximation in streams"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "the optimal sample complexity of pac learning"
    - "input sparsity and hardness for robust subspace approximation"
    - "sparse approximation via generating point sets"
    - "frequent directions simple and deterministic matrix sketching"
    - "epsilon kernel coresets for stochastic points"

Connected through hyperedge HE-5 (induced by source paper "approximating the simplicial depth"):
  with members:
    - "orthogonal range searching on the ram revisited"

Connected through hyperedge HE-6 (induced by source paper "structure aware sampling flexible and accurate summarization"):
  with members:
    - "stream sampling for variance optimal estimation of subset sums"
    - "algorithms for eps approximations of terrains"
    - "on the variance of subset sum estimation"
    - "medians and beyond new aggregation techniques for sensor networks"

Connected through hyperedge HE-7 (induced by source paper "a unified framework for approximating and clustering data"):
  with members:
    - "algorithms and hardness for subspace approximation"
    - "twice ramanujan sparsifiers"
    - "sampling algorithms and coresets for lp regression"

Connected through hyperedge HE-8 (induced by source paper "measuring independence of datasets"):
  with members:
    - "ams without 4 wise independence on product domains"

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)