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: "deepgini prioritizing massive tests to reduce labeling cost"
  Abstract: Deep neural network (DNN) based systems have been deployed to assist various tasks, including many safety-critical scenarios such as autonomous driving and medical image diagnostics. In company with the DNN-based systems' fantastic accuracy on the well-defined tasks, these systems could also exhibit incorrect behaviors and thus severe accidents and losses. Therefore, beyond the conventional accuracy-based evaluation, the testing method that can assist developers in detecting incorrect behaviors in the earlier stage is critical for quality assurance of these systems. However, given the fact that automated oracle is often not available, testing DNN-based system usually requires prohibitively expensive human efforts to label the testing data. In this paper, to reduce the efforts in labeling the testing data of DNN-based systems, we propose DeepGini, a test prioritization technique for assisting developers in identifying the tests that can reveal the incorrect behavior. DeepGini is designed based on a statistical perspective of DNN, which allows us to transform the problem of measuring the likelihood of misclassification to the problem of measuing the impurity of data set. To validate our technique, we conduct an extensive empirical study on four popular datasets. The experiment results show that DeepGini outperforms the neuron-coverage-based test prioritization in terms of both efficacy and efficiency.

Connected through hyperedge HE-1 (induced by source paper "neuralvis visualizing and interpreting deep learning models"):
  with members:
    - "neuronblocks building your nlp dnn models like playing lego"
    - "interpreting cnns via decision trees"
    - "visual interpretability for deep learning a survey"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "a preliminary study on data augmentation of deep learning for image classification"):
  with members:
    - "neuralvis visualizing and interpreting deep learning models"
    - "autoaugment learning augmentation policies from data"
    - "recent advances in recurrent neural networks"
    - "improving deep learning using generic data augmentation"
    - "random erasing data augmentation"
    - "improved regularization of convolutional neural networks with cutout"
    - "regularization for unsupervised deep neural nets"

Connected through hyperedge HE-3 (induced by source paper "from data quality to model quality an exploratory study on deep learning"):
  with members:
    - "neuralvis visualizing and interpreting deep learning models"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"
    - "network in network"

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.SE(Software Engineering)