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: "disturblabel regularizing cnn on the loss layer"
  Abstract: During a long period of time we are combating over-fitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc. In this paper, we present DisturbLabel, an extremely simple algorithm which randomly replaces a part of labels as incorrect values in each iteration. Although it seems weird to intentionally generate incorrect training labels, we show that DisturbLabel prevents the network training from over-fitting by implicitly averaging over exponentially many networks which are trained with different label sets. To the best of our knowledge, DisturbLabel serves as the first work which adds noises on the loss layer. Meanwhile, DisturbLabel cooperates well with Dropout to provide complementary regularization functions. Experiments demonstrate competitive recognition results on several popular image recognition datasets.

Connected through hyperedge HE-1 (induced by source paper "being bayesian about categorical probability"):
  with members:
    - "when does label smoothing help"
    - "momentum contrast for unsupervised visual representation learning"
    - "functional variational bayesian neural networks"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidential deep learning to quantify classification uncertainty"
    - "a variational dirichlet framework for out of distribution detection"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "does label smoothing mitigate label noise"):
  with members:
    - "regularization via structural label smoothing"
    - "are anchor points really indispensable in label noise learning"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "masking a new perspective of noisy supervision"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "on calibration of modern neural networks"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficient gan based anomaly detection"
    - "deep anomaly detection using geometric transformations"
    - "escaping from collapsing modes in a constrained space"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "regularization via structural label smoothing"):
  with members:
    - "when does label smoothing help"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to bound the multi class bayes error"
    - "robustness via curvature regularization and vice versa"
    - "adversarial logit pairing"
    - "regularizing neural networks by penalizing confident output distributions"
    - "meta learning of bounds on the bayes classifier error"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "adaptive regularization via residual smoothing in deep learning optimization"):
  with members:
    - "combining learning rate decay and weight decay with complexity gradient descent part i"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "unsupervised learning of depth and ego motion from video"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-6 (induced by source paper "pseudo labeling and confirmation bias in deep semi supervised learning"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "unsupervised label noise modeling and loss correction"
    - "semi supervised learning with scarce annotations"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "there are many consistent explanations of unlabeled data why you should average"

Connected through hyperedge HE-7 (induced by source paper "implicit semantic data augmentation for deep networks"):
  with members:
    - "convolutional networks with dense connectivity"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "ensemble soft margin softmax loss for image classification"
    - "autoaugment learning augmentation policies from data"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "attgan facial attribute editing by only changing what you want"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-8 (induced by source paper "energy confused adversarial metric learning for zero shot image retrieval and clustering"):
  with members:
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "zero shot sketch image hashing"
    - "almn deep embedding learning with geometrical virtual point generating"
    - "sampling matters in deep embedding learning"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"

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)