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: "attacking binarized neural networks"
  Abstract: Neural networks with low-precision weights and activations offer compelling efficiency advantages over their full-precision equivalents. The two most frequently discussed benefits of quantization are reduced memory consumption, and a faster forward pass when implemented with efficient bitwise operations. We propose a third benefit of very low-precision neural networks: improved robustness against some adversarial attacks, and in the worst case, performance that is on par with full-precision models. We focus on the very low-precision case where weights and activations are both quantized to $\pm$1, and note that stochastically quantizing weights in just one layer can sharply reduce the impact of iterative attacks. We observe that non-scaled binary neural networks exhibit a similar effect to the original defensive distillation procedure that led to gradient masking, and a false notion of security. We address this by conducting both black-box and white-box experiments with binary models that do not artificially mask gradients.

Connected through hyperedge HE-1 (induced by source paper "robust binary classification with the 01 loss"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "curriculum loss robust learning and generalization against label corruption"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "darts deceiving autonomous cars with toxic signs"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (induced by source paper "impact of low bitwidth quantization on the adversarial robustness for embedded neural networks"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "on evaluating adversarial robustness"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "stochastic activation pruning for robust adversarial defense"
    - "efficient two step adversarial defense for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "quantitative verification of neural networks and its security applications"):
  with members:
    - "phase transition behavior of cardinality and xor constraints"
    - "adversarial examples are a natural consequence of test error in noise"
    - "strip a defence against trojan attacks on deep neural networks"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "adversarial spheres"

Connected through hyperedge HE-4 (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"
    - "detecting adversarial samples for deep neural networks through mutation testing"
    - "towards robust training of neural networks by regularizing adversarial gradients"

Connected through hyperedge HE-5 (induced by source paper "exploring the back alleys analysing the robustness of alternative neural network architectures against adversarial attacks"):
  with members:
    - "adversarial attacks on remote user authentication using behavioural mouse dynamics"
    - "a novel method for extracting interpretable knowledge from a spiking neural classifier with time varying synaptic weights"
    - "a comprehensive analysis on adversarial robustness of spiking neural networks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "combinatorial attacks on binarized neural networks"

Connected through hyperedge HE-6 (induced by source paper "defensive quantization when efficiency meets robustness"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "l2 nonexpansive neural networks"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "defend deep neural networks against adversarial examples via fixed anddynamic quantized activation functions"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-7 (induced by source paper "discretization based solutions for secure machine learning against adversarial attacks"):
  with members:
    - "explainable learning implicit generative modelling during training for adversarial robustness"
    - "implicit generative modeling of random noise during training for adversarial robustness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples in the physical world"
    - "deep residual learning for image recognition"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-8 (induced by source paper "deepgauge multi granularity testing criteria for deep learning systems"):
  with members:
    - "spatially transformed adversarial examples"
    - "stochastic activation pruning for robust adversarial defense"
    - "revisiting the importance of individual units in cnns via ablation"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "combinatorial testing for deep learning systems"
    - "deeproad gan based metamorphic autonomous driving system testing"

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)