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: "sorting out lipschitz function approximation"
  Abstract: Training neural networks subject to a Lipschitz constraint is useful for generalization bounds, provable adversarial robustness, interpretable gradients, and Wasserstein distance estimation. By the composition property of Lipschitz functions, it suffices to ensure that each individual affine transformation or nonlinear activation function is 1-Lipschitz. The challenge is to do this while maintaining the expressive power. We identify a necessary property for such an architecture: each of the layers must preserve the gradient norm during backpropagation. Based on this, we propose to combine a gradient norm preserving activation function, GroupSort, with norm-constrained weight matrices. We show that norm-constrained GroupSort architectures are universal Lipschitz function approximators. Empirically, we show that norm-constrained GroupSort networks achieve tighter estimates of Wasserstein distance than their ReLU counterparts and can achieve provable adversarial robustness guarantees with little cost to accuracy.

Connected through hyperedge HE-1 (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"
    - "adversarial training can hurt generalization"

Connected through hyperedge HE-2 (induced by source paper "on the distance between two neural networks and the stability of learning"):
  with members:
    - "optimization for deep learning theory and algorithms"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "huggingface s transformers state of the art natural language processing"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "measuring and regularizing networks in function space"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-3 (induced by source paper "an inductive bias for distances neural nets that respect the triangle inequality"):
  with members:
    - "a projection method for metric constrained optimization"
    - "on evaluation of embodied navigation agents"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "prototypical networks for few shot learning"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"
    - "node2vec scalable feature learning for networks"
    - "input convex neural networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "generalised lipschitz regularisation equals distributional robustness"):
  with members:
    - "naive feature selection sparsity in naive bayes"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "generalizable adversarial training via spectral normalization"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-5 (induced by source paper "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretical evidence for adversarial robustness through randomization"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-6 (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"
    - "a dual approach to scalable verification of deep networks"
    - "training verified learners with learned verifiers"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "gradient descent maximizes the margin of homogeneous neural networks"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "a refined primal dual analysis of the implicit bias"
    - "implicit regularization in deep matrix factorization"
    - "fixup initialization residual learning without normalization"
    - "on exact computation with an infinitely wide neural net"
    - "implicit regularization for deep neural networks driven by an ornstein uhlenbeck like process"
    - "gradient descent finds global minima of deep neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "universal approximation with certified networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "a provable defense for deep residual networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "on the geometry of adversarial examples"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"

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)