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 training can hurt generalization"
  Abstract: While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary). Previous work has studied this tradeoff between standard and robust accuracy, but only in the setting where no predictor performs well on both objectives in the infinite data limit. In this paper, we show that even when the optimal predictor with infinite data performs well on both objectives, a tradeoff can still manifest itself with finite data. Furthermore, since our construction is based on a convex learning problem, we rule out optimization concerns, thus laying bare a fundamental tension between robustness and generalization. Finally, we show that robust self-training mostly eliminates this tradeoff by leveraging unlabeled data.

Connected through hyperedge HE-1 (induced by source paper "more data can expand the generalization gap between adversarially robust and standard models"):
  with members:
    - "adversarially robust learning could leverage computational hardness"
    - "lower bounds for adversarially robust pac learning"
    - "adversarial robustness may be at odds with simplicity"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "theoretically principled trade off between robustness and accuracy"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-2 (induced by source paper "adversarial robustness through local lipschitzness"):
  with members:
    - "randomized smoothing of all shapes and sizes"
    - "analyzing accuracy loss in randomized smoothing defenses"
    - "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"
    - "rethinking randomized smoothing for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "theoretically principled trade off between robustness and accuracy"
    - "theoretical evidence for adversarial robustness through randomization the case of the exponential family"

Connected through hyperedge HE-3 (induced by source paper "the curious case of adversarially robust models more data can help double descend or hurt generalization"):
  with members:
    - "more data can expand the generalization gap between adversarially robust and standard models"
    - "a new perspective for flexible feature gathering in scene text recognition via character anchor pooling"
    - "lower bounds for adversarially robust pac learning"
    - "two models of double descent for weak features"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "deep double descent where bigger models and more data hurt"
    - "lower bounds on adversarial robustness from optimal transport"
    - "vc classes are adversarially robustly learnable but only improperly"

Connected through hyperedge HE-4 (induced by source paper "robust binary classification with the 01 loss"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "curriculum loss robust learning and generalization against label corruption"
    - "theoretically principled trade off between robustness and accuracy"
    - "darts deceiving autonomous cars with toxic signs"
    - "attacking binarized neural networks"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-5 (induced by source paper "invariance inducing regularization using worst case transformations suffices to boost accuracy and spatial robustness"):
  with members:
    - "equivariant transformer networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "unsupervised data augmentation for consistency training"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generalisation in humans and deep neural networks"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-6 (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"
    - "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"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-7 (induced by source paper "improved sample complexities for deep networks and robust classification via an all layer margin"):
  with members:
    - "a hessian based complexity measure for deep networks"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "theoretically principled trade off between robustness and accuracy"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "implicit bias of gradient descent on linear convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "a class of parameterized loss functions for classification optimization tradeoffs and robustness characteristics"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "a tunable loss function for binary classification"
    - "a tunable measure for information leakage"
    - "rademacher complexity for adversarially robust generalization"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "context aware generative adversarial privacy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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)