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: "towards robust deep neural networks with bang"
  Abstract: Machine learning models, including state-of-the-art deep neural networks, are vulnerable to small perturbations that cause unexpected classification errors. This unexpected lack of robustness raises fundamental questions about their generalization properties and poses a serious concern for practical deployments. As such perturbations can remain imperceptible - the formed adversarial examples demonstrate an inherent inconsistency between vulnerable machine learning models and human perception - some prior work casts this problem as a security issue. Despite the significance of the discovered instabilities and ensuing research, their cause is not well understood and no effective method has been developed to address the problem. In this paper, we present a novel theory to explain why this unpleasant phenomenon exists in deep neural networks. Based on that theory, we introduce a simple, efficient, and effective training approach, Batch Adjusted Network Gradients (BANG), which significantly improves the robustness of machine learning models. While the BANG technique does not rely on any form of data augmentation or the utilization of adversarial images for training, the resultant classifiers are more resistant to adversarial perturbations while maintaining or even enhancing the overall classification performance.

Connected through hyperedge HE-1 (induced by source paper "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"

Connected through hyperedge HE-2 (induced by source paper "detecting adversarial samples using influence functions and nearest neighbors"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "defending against adversarial examples with k nearest neighbor"
    - "on evaluating adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "dnn or k nn that is the generalize vs memorize question"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial risk bounds for neural networks through sparsity based compression"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "improved generalization bounds for robust learning"
    - "generalizable adversarial training via spectral normalization"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"

Connected through hyperedge HE-4 (induced by source paper "evaluating defensive distillation for defending text processing neural networks against adversarial examples"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deep text classification can be fooled"
    - "sok security and privacy in machine learning"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "towards crafting text adversarial samples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "extending defensive distillation"
    - "adversarial examples for evaluating reading comprehension systems"

Connected through hyperedge HE-5 (induced by source paper "on the effect of low rank weights on adversarial robustness of neural networks"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "sparse dnns with improved adversarial robustness"
    - "on generation of adversarial examples using convex programming"
    - "low rank structure of learned representations"
    - "opening the black box of deep neural networks via information"
    - "a survey on deep learning in medical image analysis"
    - "estimating mixture entropy with pairwise distances"
    - "nonlinear information bottleneck"

Connected through hyperedge HE-6 (induced by source paper "built in vulnerabilities to imperceptible adversarial perturbations"):
  with members:
    - "adversarial training versus weight decay"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial spheres"
    - "adversarially robust generalization requires more data"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-7 (induced by source paper "finefool fine object contour attack via attention"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "robust physical world attacks on deep learning models"
    - "enhancing robustness of machine learning systems via data transformations"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

Connected through hyperedge HE-8 (induced by source paper "adversarial training versus weight decay"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "high dimensional spaces deep learning and adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "adversarial logit pairing"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "predicting adversarial examples with high confidence"
    - "genattack practical black box attacks with gradient free optimization"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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.CV(Computer Vision and Pattern Recognition)