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 for outlier robust regression"
  Abstract: We give the first polynomial-time algorithm for performing linear or polynomial regression resilient to adversarial corruptions in both examples and labels. #R##N#Given a sufficiently large (polynomial-size) training set drawn i.i.d. from distribution D and subsequently corrupted on some fraction of points, our algorithm outputs a linear function whose squared error is close to the squared error of the best-fitting linear function with respect to D, assuming that the marginal distribution of D over the input space is \emph{certifiably hypercontractive}. This natural property is satisfied by many well-studied distributions such as Gaussian, strongly log-concave distributions and, uniform distribution on the hypercube among others. We also give a simple statistical lower bound showing that some distributional assumption is necessary to succeed in this setting. #R##N#These results are the first of their kind and were not known to be even information-theoretically possible prior to our work. #R##N#Our approach is based on the sum-of-squares (SoS) method and is inspired by the recent applications of the method for parameter recovery problems in unsupervised learning. Our algorithm can be seen as a natural convex relaxation of the following conceptually simple non-convex optimization problem: find a linear function and a large subset of the input corrupted sample such that the least squares loss of the function over the subset is minimized over all possible large subsets.

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"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "representer point selection for explaining deep neural networks"

Connected through hyperedge HE-2 (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"
    - "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-3 (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 and lower bounds for robust linear 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-4 (induced by source paper "adversarially robust low dimensional representations"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "on robustness to adversarial examples and polynomial optimization"
    - "approximating operator norms via generalized krivine rounding"
    - "a spectral view of adversarially robust features"
    - "high dimensional robust mean estimation in nearly linear time"
    - "high accuracy low precision training"

Connected through hyperedge HE-5 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "high dimensional robust sparse regression"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "deep learning is robust to massive label noise"
    - "learning with bad training data via iterative trimmed loss minimization"

Connected through hyperedge HE-6 (induced by source paper "faster algorithms for high dimensional robust covariance estimation"):
  with members:
    - "high dimensional robust sparse regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "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-7 (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"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"
    - "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-8 (induced by source paper "high dimensional robust m estimation arbitrary corruption and heavy tails"):
  with members:
    - "high dimensional robust sparse regression"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "securing distributed machine learning in high dimensions"
    - "iteratively learning from the best"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "defending against saddle point attack in byzantine robust distributed learning"

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)