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: "on coresets for logistic regression"
  Abstract: Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable worst-case instances we introduce a complexity measure $\mu(X)$, which quantifies the hardness of compressing a data set for logistic regression. $\mu(X)$ has an intuitive statistical interpretation that may be of independent interest. For data sets with bounded $\mu(X)$-complexity, we show that a novel sensitivity sampling scheme produces the first provably sublinear $(1\pm\varepsilon)$-coreset. We illustrate the performance of our method by comparing to uniform sampling as well as to state of the art methods in the area. The experiments are conducted on real world benchmark data for logistic regression.

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"
    - "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"
    - "approximation schemes for clustering with outliers"

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"
    - "coresets and sketches"
    - "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 "sipping neural networks sensitivity informed provable pruning of neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"
    - "training support vector machines using coresets"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "on activation function coresets for network pruning"):
  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"
    - "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"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"

Connected through hyperedge HE-5 (induced by source paper "random projections and sampling algorithms for clustering of high dimensional polygonal curves"):
  with members:
    - "on the hardness of computing an average curve"
    - "the one way communication complexity of dynamic time warping distance"
    - "walking the dog fast in practice algorithm engineering of the fr echet distance"
    - "beyond worst case analysis"
    - "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"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "clustering is difficult only when it does not matter"

Connected through hyperedge HE-6 (induced by source paper "discrepancy coresets and sketches in machine learning"):
  with members:
    - "near optimal coresets of kernel density estimates"
    - "improved coresets for kernel density estimates"
    - "epsilon approximations and epsilon nets"
    - "optimal quantile approximation in streams"
    - "new frameworks for offline and streaming coreset constructions"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-7 (induced by source paper "the effectiveness of variational autoencoders for active learning"):
  with members:
    - "large scale sparse subspace clustering using landmarks"
    - "information constraints on auto encoding variational bayes"
    - "advances in variational inference"
    - "deep bayesian active learning with image data"
    - "active learning with statistical models"

Connected through hyperedge HE-8 (induced by source paper "on coresets for regularized loss minimization"):
  with members:
    - "new frameworks for offline and streaming coreset constructions"
    - "an overview of gradient descent optimization algorithms"
    - "randomized composable core sets for distributed submodular maximization"
    - "faq questions asked frequently"
    - "a unified framework for approximating and clustering 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.DS(Data Structures and Algorithms)