human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "coresets and sketches"):
  Abstract: Geometric data summarization has become an essential tool in both geometric approximation algorithms and where geometry intersects with big data problems. In linear or near-linear time large data sets can be compressed into a summary, and then more intricate algorithms can be run on the summaries whose results approximate those of the full data set. Coresets and sketches are the two most important classes of these summaries. We survey five types of coresets and sketches: shape-fitting, density estimation, high-dimensional vectors, high-dimensional point sets / matrices, and clustering.

Includes members:
  Member "coresets for k means and k median clustering and their applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rk means fast clustering for relational data")
  Member "optimal quantile approximation in streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "storyboard optimizing precomputed summaries for aggregation")
  Member "dimensionality reduction of massive sparse datasets using coresets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse coresets for svd on infinite streams")
  Member "the optimal sample complexity of pac learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond dropout feature map distortion to regularize deep neural networks")
  Member "input sparsity and hardness for robust subspace approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "sparse approximation via generating point sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate guarantees for dictionary learning")
  Member "frequent directions simple and deterministic matrix sketching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deterministic streaming sketch for ridge regression")
  Member "epsilon kernel coresets for stochastic points":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "computing shapley values for mean width in 3 d")

Question: Please tell me which class the source paper 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)