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: "adversarial sample detection for deep neural network through model mutation testing"
  Abstract: Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a normal sample with some carefully crafted human imperceptible perturbations, even highly accurate DNN make wrong decisions. Multiple defense mechanisms have been proposed which aim to hinder the generation of such adversarial samples. However, a recent work show that most of them are ineffective. In this work, we propose an alternative approach to detect adversarial samples at runtime. Our main observation is that adversarial samples are much more sensitive than normal samples if we impose random mutations on the DNN. We thus first propose a measure of 'sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity. We then integrate statistical hypothesis testing and model mutation testing to check whether an input sample is likely to be normal or adversarial at runtime by measuring its sensitivity. We evaluated our approach on the MNIST and CIFAR10 datasets. The results show that our approach detects adversarial samples generated by state-of-the-art attacking methods efficiently and accurately.

Connected through hyperedge HE-1 (induced by source paper "reliability validation of learning enabled vehicle tracking"):
  with members:
    - "test metrics for recurrent neural networks"
    - "reasoning about cognitive trust in stochastic multiagent systems"
    - "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "non intrusive detection of adversarial deep learning attacks via observer networks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "minimum uncertainty based detection of adversaries in deep neural networks"
    - "feature denoising for improving adversarial robustness"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (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"
    - "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"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"

Connected through hyperedge HE-4 (induced by source paper "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers"):
  with members:
    - "should adversarial attacks use pixel p norm"
    - "certified adversarial robustness via randomized smoothing"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "adversarial training for free"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "hardening deep neural networks via adversarial model cascades"

Connected through hyperedge HE-5 (induced by source paper "metamorphic testing for object detection systems"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "yolact real time instance segmentation"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "deep learning for generic object detection a survey"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-6 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"

Connected through hyperedge HE-7 (induced by source paper "explaining deep neural networks using spectrum based fault localization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deepfault fault localization for deep neural networks"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "an empirical study of fault localization families and their combinations"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "detecting adversarial samples for deep neural networks through mutation testing"

Connected through hyperedge HE-8 (induced by source paper "there is limited correlation between coverage and robustness for deep neural networks"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "adversarial examples opportunities and challenges"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "guiding deep learning system testing using surprise adequacy"
    - "testing deep neural networks"

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.LG(Machine Learning)