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: "dreaming more data class dependent distributions over diffeomorphisms for learned data augmentation"
  Abstract: Data augmentation is a key element in training high-dimensional models. In this approach, one synthesizes new observations by applying pre-specified transformations to the original training data; e.g.~new images are formed by rotating old ones. Current augmentation schemes, however, rely on manual specification of the applied transformations, making data augmentation an implicit form of feature engineering. With an eye towards true end-to-end learning, we suggest learning the applied transformations on a per-class basis. Particularly, we align image pairs within each class under the assumption that the spatial transformation between images belongs to a large class of diffeomorphisms. We then learn a class-specific probabilistic generative models of the transformations in a Riemannian submanifold of the Lie group of diffeomorphisms. We demonstrate significant performance improvements in training deep neural nets over manually-specified augmentation schemes. Our code and augmented datasets are available online.

Connected through hyperedge HE-1 (induced by source paper "towards fair cross domain adaptation via generative learning"):
  with members:
    - "diversity with cooperation ensemble methods for few shot classification"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "deep visual domain adaptation a survey"
    - "partial adversarial domain adaptation"
    - "bagan data augmentation with balancing gan"
    - "low shot learning from imaginary data"
    - "the frontiers of fairness in machine learning"

Connected through hyperedge HE-2 (induced by source paper "implicit label augmentation on partially annotated clips via temporally adaptive features learning"):
  with members:
    - "panoptic segmentation"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-3 (induced by source paper "learning to generate synthetic data via compositing"):
  with members:
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "spatially transformed adversarial examples"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "loss rank mining a general hard example mining method for real time detectors"
    - "modeling visual context is key to augmenting object detection datasets"
    - "unsupervised hard example mining from videos for improved object detection"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "can 3d pose be learned from 2d projections alone"

Connected through hyperedge HE-4 (induced by source paper "what else can fool deep learning addressing color constancy errors on deep neural network performance"):
  with members:
    - "sensor independent illumination estimation for dnn models"
    - "spatially transformed adversarial examples"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "autoaugment learning augmentation policies from data"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "unprocessing images for learned raw denoising"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "random erasing data augmentation"

Connected through hyperedge HE-5 (induced by source paper "sampling strategies for gan synthetic data"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "how good is my gan"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "deep facial expression recognition a survey"
    - "autoaugment learning augmentation policies from data"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-6 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "a novel orthogonal direction mesh adaptive direct search approach for svm hyperparameter tuning"
    - "randomly weighted cnns for music audio classification"
    - "environmental sound classification based on multi temporal resolution convolutional neural network combining with multi level features"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-7 (induced by source paper "data augmentation using learned transformations for one shot medical image segmentation"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "nonparametric density flows for mri intensity normalisation"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "few shot segmentation propagation with guided networks"
    - "autoaugment learning augmentation policies from data"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "error corrective boosting for learning fully convolutional networks with limited data"

Connected through hyperedge HE-8 (induced by source paper "adversarial learning of general transformations for data augmentation"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "data augmentation instead of explicit regularization"
    - "do deep nets really need weight decay and dropout"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "good semi supervised learning that requires a bad gan"
    - "triple generative adversarial nets"

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.CV(Computer Vision and Pattern Recognition)