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: "a systematic study of the class imbalance problem in convolutional neural networks"
  Abstract: Abstract   In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that completely eliminates the imbalance, whereas the optimal undersampling ratio depends on the extent of imbalance; (iv) as opposed to some classical machine learning models, oversampling does not cause overfitting of CNNs; (v) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.

Connected through hyperedge HE-1 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning"):
  with members:
    - "a study on action detection in the wild"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "dynamic curriculum learning for imbalanced data classification"
    - "decoupling representation and classifier for long tailed recognition"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "meta weight net learning an explicit mapping for sample weighting"

Connected through hyperedge HE-2 (induced by source paper "mixup regularization for region proposal based object detectors"):
  with members:
    - "augmenting data with mixup for sentence classification an empirical study"
    - "towards adversarially robust object detection"
    - "bag of freebies for training object detection neural networks"
    - "learning to generate synthetic data via compositing"
    - "on physical adversarial patches for object detection"
    - "mixmatch a holistic approach to semi supervised learning"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "regularization for deep learning a taxonomy"

Connected through hyperedge HE-3 (induced by source paper "deep representation learning on long tailed data a learnable embedding augmentation perspective"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "class balanced loss based on effective number of samples"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "gradient harmonized single stage detector"

Connected through hyperedge HE-4 (induced by source paper "analyzing differentiable fuzzy logic operators"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "semi supervised learning using differentiable reasoning"
    - "injecting prior knowledge for transfer learning into reinforcement learning algorithms using logic tensor networks"
    - "learning and t norms theory"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "deepproblog neural probabilistic logic programming"
    - "large scale gan training for high fidelity natural image synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (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"
    - "deep over sampling framework for classifying imbalanced data"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "impact of fully connected layers on performance of convolutional neural networks for image classification"):
  with members:
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "efficient neural architecture search via parameter sharing"
    - "the expressive power of neural networks a view from the width"
    - "mask r cnn"
    - "deep vs shallow networks an approximation theory perspective"
    - "learning functions when is deep better than shallow"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "dropclass and dropadapt dropping classes for deep speaker representation learning"):
  with members:
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deep speaker embedding learning with multi level pooling for text independent speaker verification"
    - "arcface additive angular margin loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-8 (induced by source paper "slice tuner a selective data collection framework for accurate and fair machine learning models"):
  with members:
    - "slice based learning a programming model for residual learning in critical data slices"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "fairgan fairness aware generative adversarial networks"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "snorkel rapid training data creation with weak supervision"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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)