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: "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.

Connected through hyperedge HE-1 (induced by source paper "meta learning for mixed linear regression"):
  with members:
    - "learning mixtures of linear regressions in subexponential time via fourier moments"
    - "sublinear optimal policy value estimation in contextual bandits"
    - "meta learning with implicit gradients"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "meta learning with latent embedding optimization"
    - "fast context adaptation via meta learning"

Connected through hyperedge HE-2 (induced by source paper "model selection for contextual bandits"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "learning to optimize under non stationarity"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "practical contextual bandits with regression oracles"
    - "make the minority great again first order regret bound for contextual bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "small loss bounds for online learning with partial information"

Connected through hyperedge HE-3 (induced by source paper "testing noisy linear functions for sparsity"):
  with members:
    - "almost optimal distribution free junta testing"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "testing low complexity affine invariant properties"
    - "lower bounds for sparse recovery"
    - "a unified framework for testing linear invariant properties"

Connected through hyperedge HE-4 (induced by source paper "sublinear optimal policy value estimation in contextual bandits"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "best arm identification in linear bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "a theory of selective prediction"):
  with members:
    - "prediction with a short memory"
    - "chasing ghosts competing with stateful policies"
    - "high confidence predictions under adversarial uncertainty"

Connected through hyperedge HE-6 (induced by source paper "estimating the fundamental limits is easier than achieving the fundamental limits"):
  with members:
    - "minimax redundancy for markov chains with large state space"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "adaptive estimation of shannon entropy"
    - "minimax estimation of functionals of discrete distributions"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

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.LG(Machine Learning)