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 "estimating learnability in the sublinear data regime"):
  Abstract: We consider the problem of estimating how well a model class is capable of fitting a distribution of labeled data. We show that it is often possible to accurately estimate this "learnability" even when given an amount of data that is too small to reliably learn any accurate model. Our first result applies to the setting where the data is drawn from a $d$-dimensional distribution with isotropic covariance (or known covariance), and the label of each datapoint is an arbitrary noisy function of the datapoint. In this setting, we show that with $O(\sqrt{d})$ samples, one can accurately estimate the fraction of the variance of the label that can be explained via the best linear function of the data. In contrast to this sublinear sample size, finding an approximation of the best-fit linear function requires on the order of $d$ samples. Our sublinear sample results and approach also extend to the non-isotropic setting, where the data distribution has an (unknown) arbitrary covariance matrix: we show that, if the label $y$ of point $x$ is a linear function with independent noise, $y = \langle x , \beta \rangle + noise$ with $\|\beta \|$ bounded, the variance of the noise can be estimated to error $\epsilon$ with $O(d^{1-1/\log{1/\epsilon}})$ if the covariance matrix has bounded condition number, or $O(d^{1-\sqrt{\epsilon}})$ if there are no bounds on the condition number. We also establish that these sample complexities are optimal, to constant factors. Finally, we extend these techniques to the setting of binary classification, where we obtain analogous sample complexities for the problem of estimating the prediction error of the best linear classifier, in a natural model of binary labeled data. We demonstrate the practical viability of our approaches on several real and synthetic datasets.

Includes members:
  Member "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning structured distributions from untrusted batches faster and simpler")
  Member "spectrum estimation from samples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "minimax estimation of functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "minimax rates of entropy estimation on large alphabets via best polynomial approximation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "to split or not to split the impact of disparate treatment in classification")
  Member "new algorithms and lower bounds for monotonicity testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "almost optimal testers for concise representations")
  Member "the power of localization for efficiently learning linear separators with noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "optimal algorithms for testing closeness of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "active property testing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improvability through semi supervised learning a survey of theoretical results")

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.LG(Machine Learning)