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: "robust regression and lasso"
  Abstract: Lasso, or $\ell^1$ regularized least squares, has been explored extensively for its remarkable sparsity properties. It is shown in this paper that the solution to Lasso, in addition to its sparsity, has robustness properties: it is the solution to a robust optimization problem. This has two important consequences. First, robustness provides a connection of the regularizer to a physical property, namely, protection from noise. This allows a principled selection of the regularizer, and in particular, generalizations of Lasso that also yield convex optimization problems are obtained by considering different uncertainty sets. #R##N#Secondly, robustness can itself be used as an avenue to exploring different properties of the solution. In particular, it is shown that robustness of the solution explains why the solution is sparse. The analysis as well as the specific results obtained differ from standard sparsity results, providing different geometric intuition. Furthermore, it is shown that the robust optimization formulation is related to kernel density estimation, and based on this approach, a proof that Lasso is consistent is given using robustness directly. Finally, a theorem saying that sparsity and algorithmic stability contradict each other, and hence Lasso is not stable, is presented.

Connected through hyperedge HE-1 (induced by source paper "a fast saddle point dynamical system approach to robust deep learning"):
  with members:
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "scaling provable adversarial defenses"
    - "trust region based adversarial attack on neural networks"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "robust optimization for non convex objectives"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-2 (induced by source paper "inductive bias of gradient descent based adversarial training on separable data"):
  with members:
    - "convergence and margin of adversarial training on separable data"
    - "feature denoising for improving adversarial robustness"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "risk and parameter convergence of logistic regression"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "adversarial example defenses ensembles of weak defenses are not strong"

Connected through hyperedge HE-3 (induced by source paper "adversarial risk bounds for neural networks through sparsity based compression"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "improved generalization bounds for robust learning"
    - "generalizable adversarial training via spectral normalization"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"

Connected through hyperedge HE-4 (induced by source paper "a saddle point dynamical system approach for robust deep learning"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "scaling provable adversarial defenses"
    - "security for machine learning based systems attacks and challenges during training and inference"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "darts deceiving autonomous cars with toxic signs"
    - "robust optimization for non convex objectives"
    - "practical black box attacks against machine learning"
    - "measuring neural net robustness with constraints"

Connected through hyperedge HE-5 (induced by source paper "reconstruction under outliers for fourier sparse functions"):
  with members:
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "efficient algorithms for outlier robust regression"
    - "robust polynomial regression up to the information theoretic limit"
    - "fourier sparse interpolation without a frequency gap"
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "analysis of boolean functions"

Connected through hyperedge HE-6 (induced by source paper "rademacher complexity for adversarially robust generalization"):
  with members:
    - "scaling provable adversarial defenses"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "adversarial vulnerability for any classifier"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "improved generalization bounds for robust learning"
    - "generalizable adversarial training via spectral normalization"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-7 (induced by source paper "efficient algorithms for outlier robust regression"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "exact tensor completion with sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "mixture models robustness and sum of squares proofs"
    - "learning from untrusted data"
    - "robust estimators in high dimensions without the computational intractability"
    - "general deviants an analysis of perturbations in compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "networked parallel algorithms for robust convex optimization via the scenario approach"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"

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.IT(Information Theory)