human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "automated testing for deep learning systems with differential behavior criteria"):
  Abstract: In this work, we conducted a study on building an automated testing system for deep learning systems based on differential behavior criteria. The automated testing goals were achieved by jointly optimizing two objective functions: maximizing differential behaviors from models under testing and maximizing neuron coverage. By observing differential behaviors from three pre-trained models during each testing iteration, the input image that triggered erroneous feedback was registered as a corner-case. The generated corner-cases can be used to examine the robustness of DNNs and consequently improve model accuracy. A project called DeepXplore was also used as a baseline model. After we fully implemented and optimized the baseline system, we explored its application as an augmenting training dataset with newly generated corner cases. With the GTRSB dataset, by retraining the model based on automated generated corner cases, the accuracy of three generic models increased by 259.2%, 53.6%, and 58.3%, respectively. Further, to extend the capability of automated testing, we explored other approaches based on differential behavior criteria to generate photo-realistic images for deep learning systems. One approach was to apply various transformations to the seed images for the deep learning framework. The other approach was to utilize the Generative Adversarial Networks (GAN) technique, which was implemented on MNIST and Driving datasets. The style transferring capability has been observed very effective in adding additional visual effects, replacing image elements, and style-shifting (virtual image to real images). The GAN-based testing sample generation system was shown to be the next frontier for automated testing for deep learning systems.

Includes members:
  Member "deeproad gan based metamorphic autonomous driving system testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a little fog for a large turn")
  Member "robust physical world attacks on deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "towards practical verification of machine learning the case of computer vision systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "certification of semantic perturbations via randomized smoothing")
  Member "deepxplore automated whitebox testing of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance driven deep learning system testing")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "robust physical world attacks on machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "deeptest automated testing of deep neural network driven autonomous cars":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "importance driven deep learning system testing")
  Member "understanding neural networks through deep visualization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adapting object detectors with conditional domain normalization")

Question: Please tell me which class the source paper 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.LG(Machine Learning)