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: "vc classes are adversarially robustly learnable but only improperly"
  Abstract: We study the question of learning an adversarially robust predictor. We show that any hypothesis class $\mathcal{H}$ with finite VC dimension is robustly PAC learnable with an improper learning rule. The requirement of being improper is necessary as we exhibit examples of hypothesis classes $\mathcal{H}$ with finite VC dimension that are not robustly PAC learnable with any proper learning rule.

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"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-2 (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"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-3 (induced by source paper "when are non parametric methods robust"):
  with members:
    - "universal bayes consistency in metric spaces"
    - "adversarially robust submodular maximization under knapsack constraints"
    - "robust decision trees against adversarial examples"
    - "on the robustness of deep k nearest neighbors"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "adversarially robust generalization requires more data"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "precise tradeoffs in adversarial training for linear regression"):
  with members:
    - "adversarial risk via optimal transport and optimal couplings"
    - "are adversarial examples inevitable"
    - "rademacher complexity for adversarially robust generalization"

Connected through hyperedge HE-5 (induced by source paper "lower bounds on adversarial robustness from optimal transport"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "theoretically principled trade off between robustness and accuracy"
    - "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"
    - "analyzing federated learning through an adversarial lens"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "adversarial spheres"

Connected through hyperedge HE-6 (induced by source paper "adversarially robust low dimensional representations"):
  with members:
    - "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"
    - "efficient algorithms for outlier robust regression"

Connected through hyperedge HE-7 (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"
    - "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"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-8 (induced by source paper "adversarially robust learning could leverage computational hardness"):
  with members:
    - "lower bounds for adversarially robust pac learning"
    - "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"
    - "adversarial examples from cryptographic pseudo random generators"

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)