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: "between class learning for image classification"
  Abstract: In this paper, we propose a novel learning method for image classification called Between-Class learning (BC learning). We generate between-class images by mixing two images belonging to different classes with a random ratio. We then input the mixed image to the model and train the model to output the mixing ratio. BC learning has the ability to impose a constraint on the shape of the feature distributions, and thus the generalization ability is improved. BC learning is originally a method developed for sounds, which can be digitally mixed. Mixing two image data does not appear to make sense; however, we argue that because convolutional neural networks have an aspect of treating input data as waveforms, what works on sounds must also work on images. First, we propose a simple mixing method using internal divisions, which surprisingly proves to significantly improve performance. Second, we propose a mixing method that treats the images as waveforms, which leads to a further improvement in performance. As a result, we achieved 19.4% and 2.26% top-1 errors on ImageNet-1K and CIFAR-10, respectively.

Connected through hyperedge HE-1 (induced by source paper "embedding propagation smoother manifold for few shot classification"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "charting the right manifold manifold mixup for few shot learning"
    - "meta learning with implicit gradients"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "few shot learning with metric agnostic conditional embeddings"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-2 (induced by source paper "understanding and enhancing mixed sample data augmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "data augmentation using random image cropping and patching for deep cnns"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "natural adversarial examples"
    - "fast autoaugment"
    - "data augmentation revisited rethinking the distribution gap between clean and augmented data"
    - "human uncertainty makes classification more robust"
    - "data augmentation by pairing samples for images classification"

Connected through hyperedge HE-3 (induced by source paper "supermix supervising the mixing data augmentation"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "relational knowledge distillation"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "variational information distillation for knowledge transfer"
    - "fast autoaugment"
    - "contrastive representation distillation"

Connected through hyperedge HE-4 (induced by source paper "data augmentation with mobius transformations"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "data augmentation using random image cropping and patching for deep cnns"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "enabling explainable fusion in deep learning with fuzzy integral neural networks"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-5 (induced by source paper "multi class novelty detection using mix up technique"):
  with members:
    - "deep transfer learning for multiple class novelty detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"
    - "hierarchical novelty detection for visual object recognition"
    - "metric learning for novelty and anomaly detection"

Connected through hyperedge HE-6 (induced by source paper "embedding expansion augmentation in embedding space for deep metric learning"):
  with members:
    - "improved embeddings with easy positive triplet mining"
    - "in defense of the triplet loss for person re identification"
    - "mixup beyond empirical risk minimization"
    - "deep variational information bottleneck"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "cutmix regularization strategy to train strong classifiers with localizable features"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "dropblock a regularization method for convolutional networks"
    - "improved mixed example data augmentation"
    - "modeling visual context is key to augmenting object detection datasets"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial complementary learning for weakly supervised object localization"
    - "shakedrop regularization for deep residual learning"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-8 (induced by source paper "data augmentation revisited rethinking the distribution gap between clean and augmented data"):
  with members:
    - "implicit semantic data augmentation for deep networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "fast autoaugment"
    - "bag of tricks for image classification with convolutional neural networks"
    - "a kernel theory of modern data augmentation"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

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)