human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generalizing to unseen domains via adversarial data augmentation"):
  Abstract: We are concerned with learning models that generalize well to different \emph{unseen} domains. We consider a worst-case formulation over data distributions that are near the source domain in the feature space. Only using training data from a single source distribution, we propose an iterative procedure that augments the dataset with examples from a fictitious target domain that is "hard" under the current model. We show that our iterative scheme is an adaptive data augmentation method where we append adversarial examples at each iteration. For softmax losses, we show that our method is a data-dependent regularization scheme that behaves differently from classical regularizers that regularize towards zero (e.g., ridge or lasso). On digit recognition and semantic segmentation tasks, our method learns models improve performance across a range of a priori unknown target domains.

Includes members:
  Member "robust place categorization with deep domain generalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to see across domains and modalities")
  Member "minimax statistical learning with wasserstein distances":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift")
  Member "domain randomization for transferring deep neural networks from simulation to the real world":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "adversarial feature augmentation for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative pseudo label refinement for unsupervised domain adaptation")
  Member "deeper broader and artier domain generalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "a baseline for detecting misclassified and out of distribution examples in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

Question: Please tell me which class the source paper 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.CV(Computer Vision and Pattern Recognition)