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: "cost sensitive learning of deep feature representations from imbalanced data"
  Abstract: Class imbalance is a common problem in the case of real-world object detection and classification tasks. Data of some classes is abundant making them an over-represented majority, and data of other classes is scarce, making them an under-represented minority. This imbalance makes it challenging for a classifier to appropriately learn the discriminating boundaries of the majority and minority classes. In this work, we propose a cost sensitive deep neural network which can automatically learn robust feature representations for both the majority and minority classes. During training, our learning procedure jointly optimizes the class dependent costs and the neural network parameters. The proposed approach is applicable to both binary and multi-class problems without any modification. Moreover, as opposed to data level approaches, we do not alter the original data distribution which results in a lower computational cost during the training process. We report the results of our experiments on six major image classification datasets and show that the proposed approach significantly outperforms the baseline algorithms. Comparisons with popular data sampling techniques and cost sensitive classifiers demonstrate the superior performance of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "learning from multiple experts self paced knowledge distillation for long tailed classification"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "imbalance problems in object detection a review"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "learning to reweight examples for robust deep learning"
    - "evolutionary data measures understanding the difficulty of text classification tasks"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-2 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems"):
  with members:
    - "self ensembling with gan based data augmentation for domain adaptation in semantic segmentation"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "the ethical dilemma when not setting up cost based decision rules in semantic segmentation"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "autoaugment learning augmentation policies from data"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"

Connected through hyperedge HE-3 (induced by source paper "class imbalanced semi supervised learning"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "mixmatch a holistic approach to semi supervised learning"
    - "imbalanced deep learning by minority class incremental rectification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "manifold regularization with gans for semi supervised learning"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "deep over sampling framework for classifying imbalanced data"

Connected through hyperedge HE-4 (induced by source paper "automatic hierarchical classification of kelps using deep residual features"):
  with members:
    - "resfeats residual network based features for image classification"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "kernel density estimation based sampling for imbalanced class distribution"):
  with members:
    - "outlier detection in high dimensional data"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "image super resolution as a defense against adversarial attacks"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "adversarial attacks and defences competition"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "video salient object detection via fully convolutional networks"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"

Connected through hyperedge HE-7 (induced by source paper "data augmentation for object detection via progressive and selective instance switching"):
  with members:
    - "data augmentation using random image cropping and patching for deep cnns"
    - "dynamic curriculum learning for imbalanced data classification"
    - "class balanced loss based on effective number of samples"
    - "learning to segment via cut and paste"
    - "data augmentation by pairing samples for images classification"
    - "on the importance of visual context for data augmentation in scene understanding"
    - "compositional gan learning conditional image composition"
    - "modeling visual context is key to augmenting object detection datasets"

Connected through hyperedge HE-8 (induced by source paper "on class imbalance and background filtering in visual relationship detection"):
  with members:
    - "graph r cnn for scene graph generation"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "visual relationship prediction via label clustering and incorporation of depth information"
    - "image generation from scene graphs"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "vip cnn visual phrase guided convolutional neural network"
    - "on calibration of modern neural networks"

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)