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: "detecting adversarial samples for deep neural networks through mutation testing"
  Abstract: Recently, it has been shown that deep neural networks (DNN) are subject to attacks through adversarial samples. Adversarial samples are often crafted through adversarial perturbation, i.e., manipulating the original sample with minor modifications so that the DNN model labels the sample incorrectly. Given that it is almost impossible to train perfect DNN, adversarial samples are shown to be easy to generate. As DNN are increasingly used in safety-critical systems like autonomous cars, it is crucial to develop techniques for defending such attacks. Existing defense mechanisms which aim to make adversarial perturbation challenging have been shown to be ineffective. In this work, we propose an alternative approach. We first observe that adversarial samples are much more sensitive to perturbations than normal samples. That is, if we impose random perturbations on a normal and an adversarial sample respectively, there is a significant difference between the ratio of label change due to the perturbations. Observing this, we design a statistical adversary detection algorithm called nMutant (inspired by mutation testing from software engineering community). Our experiments show that nMutant effectively detects most of the adversarial samples generated by recently proposed attacking methods. Furthermore, we provide an error bound with certain statistical significance along with the detection.

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"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "input validation for neural networks via runtime local robustness verification"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "outside the box abstraction based monitoring of neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "soter programming safe robotics system using runtime assurance"
    - "soter a runtime assurance framework for programming safe robotics systems"
    - "magnet a two pronged defense against adversarial examples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial sample detection for deep neural network through model mutation testing"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "spatially transformed adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "towards robust training of neural networks by regularizing adversarial gradients"
    - "attacking convolutional neural network using differential evolution"

Connected through hyperedge HE-4 (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"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "test metrics for recurrent neural networks"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "recurrent neural networks for time series forecasting"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "on adversarial examples for character level neural machine translation"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "symbolic execution for deep neural networks"
    - "towards dependability metrics for neural networks"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-6 (induced by source paper "leveraging model interpretability and stability to increase model robustness"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "interpreting cnns via decision trees"
    - "how important is a neuron"
    - "computationally efficient measures of internal neuron importance"
    - "neural network interpretation via fine grained textual summarization"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-7 (induced by source paper "input prioritization for testing neural networks"):
  with members:
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"
    - "guiding deep learning system testing using surprise adequacy"
    - "deeproad gan based metamorphic autonomous driving system testing"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-8 (induced by source paper "toward robust image classification"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "extending defensive distillation"
    - "countering adversarial images using input transformations"
    - "adversarial diversity and hard positive generation"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

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)