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: "improved generalization bounds for robust learning"
  Abstract: We consider a model of robust learning in an adversarial environment. The learner gets uncorrupted training data with access to possible corruptions that may be used by the adversary during testing. Their aim is to build a robust classifier that would be tested on future adversarially corrupted data. We use a zero-sum game between the learner and the adversary as our game theoretic framework. The adversary is limited to $k$ possible corruptions for each input. Our model is closely related to the adversarial examples model of one of Schmidt et al. (2018) and Madry et al. (2017). #R##N#We refer to binary and multi-class classification settings, and regression setting. Our main results are generalization bounds for all settings. For the binary classification setting, we improve a generalization bound previously found in Feige et al. (2015). We generalize to the case of weighted average of hypotheses from $H$ that is not limited to be finite. The sample complexity has been improved from $\O(\frac{1}{\epsilon^4}\log(\frac{|H|}{\delta}))$ to $\O(\frac{1}{\epsilon^2}(k\log(k)VC(H)+\log\frac{1}{\delta}))$. The core of all is proofs based on bounds of the empirical Rademacher complexity. #R##N#For the binary classification, we use a known regret minimization algorithm of Feige et al. that uses an ERM oracle as a blackbox and we expand on the multi-class and regression settings. The algorithm provides us near optimal policies for the players on a given training sample. The learner starts with a fixed hypothesis class $H$ and chooses a convex combination of hypotheses from $H$. The learner's loss is measured on adversarial corrupted inputs. #R##N#Along the way, we obtain results on fat-shattering dimension and Rademacher complexity of $k$-fold maxima over function classes; these may be of independent interest.

Connected through hyperedge HE-1 (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-2 (induced by source paper "are labels required for improving adversarial robustness"):
  with members:
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "unsupervised data augmentation for consistency training"
    - "semi supervised learning with scarce annotations"
    - "using pre training can improve model robustness and uncertainty"
    - "mixmatch a holistic approach to semi supervised learning"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-3 (induced by source paper "empirically measuring concentration fundamental limits on intrinsic robustness"):
  with members:
    - "understanding the un interpretability of natural image distributions using generative models"
    - "lower bounds on adversarial robustness from optimal transport"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "adversarial training can hurt generalization"
    - "scaling provable adversarial defenses"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"

Connected through hyperedge HE-4 (induced by source paper "adversarially robust learning could leverage computational hardness"):
  with members:
    - "lower bounds for adversarially robust pac learning"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "scaling provable adversarial defenses"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "can adversarially robust learning leverage computational hardness"
    - "adversarial spheres"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-5 (induced by source paper "lower bounds for adversarially robust pac learning"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "empirically measuring concentration fundamental limits on intrinsic robustness"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "adversarial spheres"
    - "adversarial examples from cryptographic pseudo random generators"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-6 (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"
    - "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"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-7 (induced by source paper "adversarial risk via optimal transport and optimal couplings"):
  with members:
    - "lower bounds for adversarially robust pac learning"
    - "on the hardness of robust classification"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "robustness via curvature regularization and vice versa"
    - "adversarial spheres"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-8 (induced by source paper "on robustness to adversarial examples and polynomial optimization"):
  with members:
    - "on the hardness of robust classification"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "adversarial examples from cryptographic pseudo random generators"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "certified defenses against adversarial examples"
    - "motivating the rules of the game for adversarial example research"
    - "robustness of classifiers from adversarial to random noise"
    - "analysis of boolean functions"

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)