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: "improved regularization of convolutional neural networks with cutout"
  Abstract: Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "on feature normalization and data augmentation"):
  with members:
    - "convolutional networks with dense connectivity"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "implicit semantic data augmentation for deep networks"
    - "pay less attention with lightweight and dynamic convolutions"
    - "bertscore evaluating text generation with bert"
    - "ctrl a conditional transformer language model for controllable generation"
    - "natural adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "deep double descent where bigger models and more data hurt"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "me net towards effective adversarial robustness with matrix estimation"

Connected through hyperedge HE-3 (induced by source paper "bats binary architecture search"):
  with members:
    - "learning architectures for binary networks"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "back to simplicity how to train accurate bnns from scratch"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-4 (induced by source paper "hyper parameter optimization a review of algorithms and applications"):
  with members:
    - "a generalized framework for population based training"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "meta learning a survey"
    - "deep learning using rectified linear units relu"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "massively parallel hyperparameter tuning"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "faster autoaugment learning augmentation strategies using backpropagation"
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "efficientdet scalable and efficient object detection"

Connected through hyperedge HE-6 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "joint discriminative and generative learning for person re identification"
    - "consistency regularization for generative adversarial networks"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-7 (induced by source paper "cross domain few shot classification via learned feature wise transformation"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "feature critic networks for heterogeneous domain generalization"
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "progressive domain adaptation for object detection"
    - "a closer look at few shot classification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "guided image to image translation with bi directional feature transformation"

Connected through hyperedge HE-8 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking"):
  with members:
    - "gridmask data augmentation"
    - "reppoints point set representation for object detection"
    - "res2net a new multi scale backbone architecture"
    - "foveabox beyond anchor based object detector"
    - "making convolutional networks shift invariant again"
    - "region proposal by guided anchoring"
    - "mixconv mixed depthwise convolutional kernels"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"

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)