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: "deepgauge multi granularity testing criteria for deep learning systems"
  Abstract: Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.

Connected through hyperedge HE-1 (induced by source paper "importance driven deep learning system testing"):
  with members:
    - "input prioritization for testing neural networks"
    - "deepfault fault localization for deep neural networks"
    - "machine learning testing survey landscapes and horizons"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"

Connected through hyperedge HE-2 (induced by source paper "raid randomized adversarial input detection for neural networks"):
  with members:
    - "when explainability meets adversarial learning detecting adversarial examples using shap signatures"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "deepsmartfuzzer reward guided test generation for deep learning"
    - "deepfault fault localization for deep neural networks"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "on evaluating adversarial robustness"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "reludiff differential verification of deep neural networks"):
  with members:
    - "towards compact and robust deep neural networks"
    - "deep neural network compression for aircraft collision avoidance systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "concolic testing for deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "efficient formal safety analysis of neural networks"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "manifold for machine learning assurance"):
  with members:
    - "input prioritization for testing neural networks"
    - "diagnosing and enhancing vae models"
    - "machine learning testing survey landscapes and horizons"
    - "guiding deep learning system testing using surprise adequacy"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "emnist an extension of mnist to handwritten letters"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-5 (induced by source paper "text a 3 activation anomaly analysis"):
  with members:
    - "dla dense layer analysis for adversarial example detection"
    - "deep learning for anomaly detection a survey"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "lstm based encoder decoder for multi sensor anomaly detection"

Connected through hyperedge HE-6 (induced by source paper "testing dnn image classifier for confusion bias errors"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "metric learning for adversarial robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "noise tolerant fair classification"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "visual interpretability for deep learning a survey"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-7 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "concolic testing for deep neural networks"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"

Connected through hyperedge HE-8 (induced by source paper "dla dense layer analysis for adversarial example detection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial attacks and defences a survey"
    - "audio adversarial examples targeted attacks on speech to text"
    - "testing deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

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)