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: "efficient algorithms and lower bounds for robust linear regression"
  Abstract: We study the problem of high-dimensional linear regression in a robust model where an $\epsilon$-fraction of the samples can be adversarially corrupted. We focus on the fundamental setting where the covariates of the uncorrupted samples are drawn from a Gaussian distribution $\mathcal{N}(0, \Sigma)$ on $\mathbb{R}^d$. We give nearly tight upper bounds and computational lower bounds for this problem. Specifically, our main contributions are as follows: #R##N#For the case that the covariance matrix is known to be the identity, we give a sample near-optimal and computationally efficient algorithm that outputs a candidate hypothesis vector $\widehat{\beta}$ which approximates the unknown regression vector $\beta$ within $\ell_2$-norm $O(\epsilon \log(1/\epsilon) \sigma)$, where $\sigma$ is the standard deviation of the random observation noise. An error of $\Omega (\epsilon \sigma)$ is information-theoretically necessary, even with infinite sample size. Prior work gave an algorithm for this problem with sample complexity $\tilde{\Omega}(d^2/\epsilon^2)$ whose error guarantee scales with the $\ell_2$-norm of $\beta$. #R##N#For the case of unknown covariance, we show that we can efficiently achieve the same error guarantee as in the known covariance case using an additional $\tilde{O}(d^2/\epsilon^2)$ unlabeled examples. On the other hand, an error of $O(\epsilon \sigma)$ can be information-theoretically attained with $O(d/\epsilon^2)$ samples. We prove a Statistical Query (SQ) lower bound providing evidence that this quadratic tradeoff in the sample size is inherent. More specifically, we show that any polynomial time SQ learning algorithm for robust linear regression (in Huber's contamination model) with estimation complexity $O(d^{2-c})$, where $c>0$ is an arbitrarily small constant, must incur an error of $\Omega(\sqrt{\epsilon} \sigma)$.

Connected through hyperedge HE-1 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "on defending against label flipping attacks on malware detection systems"
    - "certified adversarial robustness via randomized smoothing"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "representer point selection for explaining deep neural networks"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-2 (induced by source paper "robust mean estimation under coordinate level corruption"):
  with members:
    - "faster algorithms for high dimensional robust covariance estimation"
    - "holodetect few shot learning for error detection"
    - "how hard is robust mean estimation"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "holoclean holistic data repairs with probabilistic inference"
    - "learning from untrusted data"

Connected through hyperedge HE-3 (induced by source paper "faster algorithms for high dimensional robust covariance estimation"):
  with members:
    - "high dimensional robust sparse regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"

Connected through hyperedge HE-4 (induced by source paper "nearly tight bounds for robust proper learning of halfspaces with a margin"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "hardness of learning noisy halfspaces using polynomial thresholds"

Connected through hyperedge HE-5 (induced by source paper "outlier robust high dimensional sparse estimation via iterative filtering"):
  with members:
    - "high dimensional robust estimation of sparse models via trimmed hard thresholding"
    - "high dimensional robust sparse regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "certified defenses for data poisoning attacks"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-6 (induced by source paper "list decodable learning via sum of squares"):
  with members:
    - "list decodable linear regression"
    - "efficient algorithms for outlier robust regression"
    - "outlier robust moment estimation via sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "mixture models robustness and sum of squares proofs"
    - "learning from untrusted data"
    - "robust estimators in high dimensions without the computational intractability"

Connected through hyperedge HE-7 (induced by source paper "distribution independent pac learning of halfspaces with massart noise"):
  with members:
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "agnostic estimation of mean and covariance"

Connected through hyperedge HE-8 (induced by source paper "on mean estimation for general norms with statistical queries"):
  with members:
    - "towards a zero one law for entrywise low rank approximation"
    - "subspace embedding and linear regression with orlicz norm"
    - "on the complexity of learning neural networks"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "approximate near neighbors for general symmetric norms"
    - "a general characterization of the statistical query complexity"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "streaming symmetric norms via measure concentration"

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)