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: "data dropout optimizing training data for convolutional neural networks"
  Abstract: Deep learning models learn to fit training data while they are highly expected to generalize well to testing data. Most works aim at finding such models by creatively designing architectures and fine-tuning parameters. To adapt to particular tasks, hand-crafted information such as image prior has also been incorporated into end-to-end learning. However, very little progress has been made on investigating how an individual training sample will influence the generalization ability of a model. In other words, to achieve high generalization accuracy, do we really need all the samples in a training dataset? In this paper, we demonstrate that deep learning models such as convolutional neural networks may not favor all training samples, and generalization accuracy can be further improved by dropping those unfavorable samples. Specifically, the influence of removing a training sample is quantifiable, and we propose a Two-Round Training approach, aiming to achieve higher generalization accuracy. We locate unfavorable samples after the first round of training, and then retrain the model from scratch with the reduced training dataset in the second round. Since our approach is essentially different from fine-tuning or further training, the computational cost should not be a concern. Our extensive experimental results indicate that, with identical settings, the proposed approach can boost performance of the well-known networks on both high-level computer vision problems such as image classification, and low-level vision problems such as image denoising.

Connected through hyperedge HE-1 (induced by source paper "influence function based data poisoning attacks to top n recommender systems"):
  with members:
    - "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach"
    - "byzantine resilient stochastic gradient descent for distributed learning a lipschitz inspired coordinate wise median approach"
    - "on the accuracy of influence functions for measuring group effects"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "attacking graph based classification via manipulating the graph structure"
    - "poisoning attacks to graph based recommender systems"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"

Connected through hyperedge HE-2 (induced by source paper "suppressing model overfitting for image super resolution networks"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "blind super resolution with iterative kernel correction"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "investigating backtranslation in neural machine translation"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "data augmentation by pairing samples for images classification"

Connected through hyperedge HE-3 (induced by source paper "distribution density tails and outliers in machine learning metrics and applications"):
  with members:
    - "less is more culling the training set to improve robustness of deep neural networks"
    - "learning to reweight examples for robust deep learning"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "biased importance sampling for deep neural network training"

Connected through hyperedge HE-4 (induced by source paper "less is better unweighted data subsampling via influence function"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "training set debugging using trusted items"
    - "learning to reweight examples for robust deep learning"
    - "finding influential training samples for gradient boosted decision trees"
    - "focal loss for dense object detection"
    - "axiomatic characterization of data driven influence measures for classification"
    - "recommendations as treatments debiasing learning and evaluation"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "instance based deep transfer learning"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "emnist an extension of mnist to handwritten letters"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "deep transfer learning with joint adaptation networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)