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: "adversarial examples from cryptographic pseudo random generators"
  Abstract: In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the problem. More precisely, we constructed a binary classification task for which (i) a robust classifier exists; yet no non-trivial accuracy can be obtained with an efficient algorithm in (ii) the statistical query model. In the present paper we significantly strengthen both (i) and (ii): we now construct a task which admits (i') a maximally robust classifier (that is it can tolerate perturbations of size comparable to the size of the examples themselves); and moreover we prove computational hardness of learning this task under (ii') a standard cryptographic assumption.

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 "theoretically principled trade off between robustness and accuracy"):
  with members:
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "adversarial training for free"
    - "scaling provable adversarial defenses"
    - "spatially transformed adversarial examples"
    - "adversarial vision challenge"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "certified adversarial robustness via randomized smoothing"):
  with members:
    - "certifiably robust interpretation in deep learning"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "scaling provable adversarial defenses"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "sorting out lipschitz function approximation"
    - "a dual approach to scalable verification of deep networks"
    - "training verified learners with learned verifiers"

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 risk and robustness general definitions and implications for the uniform distribution"
    - "improved generalization bounds for robust learning"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-6 (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 risk and robustness general definitions and implications for the uniform distribution"
    - "improved generalization bounds for robust learning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "on the hardness of robust classification"):
  with members:
    - "a formalization of robustness for deep neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial vulnerability for any classifier"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "are adversarial examples inevitable"
    - "robustness of classifiers from adversarial to random noise"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-8 (induced by source paper "how hard is robust mean estimation"):
  with members:
    - "being robust in high dimensions can be practical"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "hypercontractivity sum of squares proofs and their applications"

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)