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: "learning with a wasserstein loss"
  Abstract: Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions. In this paper we develop a loss function for multi-label learning, based on the Wasserstein distance. The Wasserstein distance provides a natural notion of dissimilarity for probability measures. Although optimizing with respect to the exact Wasserstein distance is costly, recent work has described a regularized approximation that is efficiently computed. We describe an efficient learning algorithm based on this regularization, as well as a novel extension of the Wasserstein distance from probability measures to unnormalized measures. We also describe a statistical learning bound for the loss. The Wasserstein loss can encourage smoothness of the predictions with respect to a chosen metric on the output space. We demonstrate this property on a real-data tag prediction problem, using the Yahoo Flickr Creative Commons dataset, outperforming a baseline that doesn't use the metric.

Connected through hyperedge HE-1 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "improving sequence to sequence learning via optimal transport"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "dropblock a regularization method for convolutional networks"
    - "policy optimization as wasserstein gradient flows"
    - "understanding batch normalization"
    - "decorrelated batch normalization"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-2 (induced by source paper "statistical optimal transport posed as learning kernel embedding"):
  with members:
    - "kernel conditional density operators"
    - "hierarchical optimal transport for document representation"
    - "kernel wasserstein distance"
    - "gromov wasserstein alignment of word embedding spaces"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "tsallis regularized optimal transport and ecological inference"
    - "on integral probability metrics φ divergences and binary classification"

Connected through hyperedge HE-3 (induced by source paper "on unbalanced optimal transport an analysis of sinkhorn algorithm"):
  with members:
    - "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"
    - "on the acceleration of the sinkhorn and greenkhorn algorithms for optimal transport"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "towards optimal running times for optimal transport"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

Connected through hyperedge HE-4 (induced by source paper "regularization helps with mitigating poisoning attacks distributionally robust machine learning using the wasserstein distance"):
  with members:
    - "a game theoretic approach to adversarial linear support vector classification"
    - "mixed strategy game model against data poisoning attacks"
    - "certified defenses for data poisoning attacks"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-5 (induced by source paper "sliced wasserstein discrepancy for unsupervised domain adaptation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "variational wasserstein clustering"
    - "learning to adapt structured output space for semantic segmentation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "open set domain adaptation by backpropagation"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "generative modeling using the sliced wasserstein distance"

Connected through hyperedge HE-6 (induced by source paper "wasserstein adversarial regularization war on label noise"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "how does disagreement help generalization against label corruption"
    - "adversarial robustness via adversarial label smoothing"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "the devil of face recognition is in the noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"

Connected through hyperedge HE-7 (induced by source paper "on scalable and efficient computation of large scale optimal transport"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "spectral normalization for generative adversarial networks"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-8 (induced by source paper "directional adversarial training for cost sensitive deep learning classification applications"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"
    - "adversarial vulnerability for any classifier"
    - "adversarially robust generalization requires more data"
    - "a mathematical theory of semantic development in deep neural 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)