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: "can we gain more from orthogonality regularizations in training deep cnns"
  Abstract: This paper seeks to answer the question: as the (near-) orthogonality of weights is found to be a favorable property for training deep convolutional neural networks, how can we enforce it in more effective and easy-to-use ways? We develop novel orthogonality regularizations on training deep CNNs, utilizing various advanced analytical tools such as mutual coherence and restricted isometry property. These plug-and-play regularizations can be conveniently incorporated into training almost any CNN without extra hassle. We then benchmark their effects on state-of-the-art models: ResNet, WideResNet, and ResNeXt, on several most popular computer vision datasets: CIFAR-10, CIFAR-100, SVHN and ImageNet. We observe consistent performance gains after applying those proposed regularizations, in terms of both the final accuracies achieved, and faster and more stable convergences. We have made our codes and pre-trained models publicly available: this https URL

Connected through hyperedge HE-1 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters"):
  with members:
    - "orthogonal deep neural networks"
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "mdfn multi scale deep feature learning network for object detection"
    - "towards learning affine invariant representations via data efficient cnns"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "interpretable convolutional neural networks via feedforward design"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"

Connected through hyperedge HE-2 (induced by source paper "on isometry robustness of deep 3d point cloud models under adversarial attacks"):
  with members:
    - "adversarial attack and defense on point sets"
    - "effective rotation invariant point cnn with spherical harmonics kernels"
    - "srinet learning strictly rotation invariant representations for point cloud classification and segmentation"
    - "robustness of 3d deep learning in an adversarial setting"
    - "theoretically principled trade off between robustness and accuracy"
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "relation shape convolutional neural network for point cloud analysis"
    - "modeling local geometric structure of 3d point clouds using geo cnn"

Connected through hyperedge HE-3 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform"):
  with members:
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "riemannian adaptive optimization methods"
    - "riemannian approach to batch normalization"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "dizzyrnn reparameterizing recurrent neural networks for norm preserving backpropagation"
    - "wide residual networks"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "abd net attentive but diverse person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "feature mask network for person re identification"
    - "attribute aware attention model for fine grained representation learning"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "triplet based deep similarity learning for person re identification"

Connected through hyperedge HE-5 (induced by source paper "orthogonal convolutional neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "autogan neural architecture search for generative adversarial networks"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "simple black box adversarial attacks"
    - "superposition of many models into one"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-6 (induced by source paper "plug and play methods provably converge with properly trained denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "denoising prior driven deep neural network for image restoration"
    - "regularization by denoising clarifications and new interpretations"
    - "regularized fourier ptychography using an online plug and play algorithm"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"

Connected through hyperedge HE-7 (induced by source paper "decoder choice network for meta learning"):
  with members:
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "learning to support exploiting structure information in support sets for one shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "snapshot ensembles train 1 get m for free"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-8 (induced by source paper "incremental multi domain learning with network latent tensor factorization"):
  with members:
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "learning to generalize meta learning for domain generalization"
    - "incremental learning through deep adaptation"
    - "adversarial discriminative domain adaptation"
    - "learning multiple visual domains with residual adapters"

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)