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: "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.

Connected through hyperedge HE-1 (induced by source paper "layered sampling for robust optimization problems"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "iterative least trimmed squares for mixed linear regression"
    - "a practical algorithm for distributed clustering and outlier detection"
    - "on coresets for logistic regression"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "efficient algorithms for outlier robust regression"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "sobol tensor trains for global sensitivity analysis"

Connected through hyperedge HE-2 (induced by source paper "sets clustering"):
  with members:
    - "introduction to coresets accurate coresets"
    - "tight sensitivity bounds for smaller coresets"
    - "k means clustering of lines for big data"
    - "on coresets for logistic regression"
    - "new frameworks for offline and streaming coreset constructions"
    - "k means for streaming and distributed big sparse data"
    - "improved and simplified inapproximability for k means"
    - "local search based approximation algorithms for mobile facility location problems"

Connected through hyperedge HE-3 (induced by source paper "storyboard optimizing precomputed summaries for aggregation"):
  with members:
    - "hillview a trillion cell spreadsheet for big data"
    - "ddsketch a fast and fully mergeable quantile sketch with relative error guarantees"
    - "moment based quantile sketches for efficient high cardinality aggregation queries"
    - "macrobase prioritizing attention in fast data"
    - "optimal quantile approximation in streams"
    - "structure aware sampling flexible and accurate summarization"
    - "unbiased matrix rounding"

Connected through hyperedge HE-4 (induced by source paper "anchor transform learning sparse representations of discrete objects"):
  with members:
    - "neural input search for large scale recommendation models"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "word embedding based on low rank doubly stochastic matrix decomposition"
    - "bayesian compression for natural language processing"
    - "probabilistic fasttext for multi sense word embeddings"
    - "adaptive input representations for neural language modeling"
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "data independent neural pruning via coresets"):
  with members:
    - "the state of sparsity in deep neural networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "a convergence theory for deep learning via over parameterization"
    - "coreset based neural network compression"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-6 (induced by source paper "on activation function coresets for network pruning"):
  with members:
    - "the state of sparsity in deep neural networks"
    - "on coresets for logistic regression"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "a convergence theory for deep learning via over parameterization"
    - "coreset based neural network compression"
    - "nisp pruning networks using neuron importance score propagation"
    - "compression aware training of deep networks"

Connected through hyperedge HE-7 (induced by source paper "coresets for clustering in graphs of bounded treewidth"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "coresets for ordered weighted clustering"
    - "k means clustering of lines for big data"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "new frameworks for offline and streaming coreset constructions"

Connected through hyperedge HE-8 (induced by source paper "robust coreset construction for distributed machine learning"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "federated learning strategies for improving communication efficiency"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "federated multi task learning"
    - "new frameworks for offline and streaming coreset constructions"
    - "communication efficient learning of deep networks from decentralized data"
    - "geometric median in nearly linear time"
    - "sketching meets random projection in the dual a provable recovery algorithm for big and high dimensional data"

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)