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: "wasserstein distributional robustness and regularization in statistical learning"
  Abstract: A central question in statistical learning is to design algorithms that not only perform well on training data, but also generalize to new and unseen data. In this paper, we tackle this question by formulating a distributionally robust stochastic optimization (DRSO) problem, which seeks a solution that minimizes the worst-case expected loss over a family of distributions that are close to the empirical distribution in Wasserstein distances. We establish a connection between such Wasserstein DRSO and regularization. More precisely, we identify a broad class of loss functions, for which the Wasserstein DRSO is asymptotically equivalent to a regularization problem with a gradient-norm penalty. Such relation provides new interpretations for problems involving regularization, including a great number of statistical learning problems and discrete choice models (e.g. multinomial logit). The connection suggests a principled way to regularize high-dimensional, non-convex problems. This is demonstrated through two applications: the training of Wasserstein generative adversarial networks (WGANs) in deep learning, and learning heterogeneous consumer preferences with mixed logit choice model.

Connected through hyperedge HE-1 (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-2 (induced by source paper "wasserstein diffusion tikhonov regularization"):
  with members:
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "improved robustness to adversarial examples using lipschitz regularization of the loss"
    - "are adversarial examples inevitable"
    - "adversarial defense via data dependent activation function and total variation minimization"
    - "deep defense training dnns with improved adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "stein bridging enabling mutual reinforcement between explicit and implicit generative models"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "a variational inequality perspective on generative adversarial nets"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "learning implicit generative models by teaching explicit ones"
    - "large scale gan training for high fidelity natural image synthesis"
    - "calibrating energy based generative adversarial networks"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "incorporating unlabeled data into distributionally robust learning"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "audio adversarial examples targeted attacks on speech to text"
    - "robust physical world attacks on deep learning models"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "end to end learning for self driving cars"
    - "an empirical evaluation of deep learning on highway driving"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "fast and robust rank aggregation against model misspecification"):
  with members:
    - "generalizing to unseen domains via adversarial data augmentation"
    - "data driven rank breaking for efficient rank aggregation"
    - "peer grading in a course on algorithms and data structures machine learning algorithms do not improve over simple baselines"
    - "methods for ordinal peer grading"

Connected through hyperedge HE-6 (induced by source paper "distributionally robust optimization and generalization in kernel methods"):
  with members:
    - "distributionally robust submodular maximization"
    - "generative moment matching networks"
    - "intriguing properties of neural networks"
    - "robustness and regularization of support vector machines"

Connected through hyperedge HE-7 (induced by source paper "robust gans against dishonest adversaries"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "robust conditional generative adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)