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: "suppressing the unusual towards robust cnns using symmetric activation functions"
  Abstract: Many deep Convolutional Neural Networks (CNN) make incorrect predictions on adversarial samples obtained by imperceptible perturbations of clean samples. We hypothesize that this is caused by a failure to suppress unusual signals within network layers. As remedy we propose the use of Symmetric Activation Functions (SAF) in non-linear signal transducer units. These units suppress signals of exceptional magnitude. We prove that SAF networks can perform classification tasks to arbitrary precision in a simplified situation. In practice, rather than use SAFs alone, we add them into CNNs to improve their robustness. The modified CNNs can be easily trained using popular strategies with the moderate training load. Our experiments on MNIST and CIFAR-10 show that the modified CNNs perform similarly to plain ones on clean samples, and are remarkably more robust against adversarial and nonsense samples.

Connected through hyperedge HE-1 (induced by source paper "motivating the rules of the game for adversarial example research"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "machine vs machine minimax optimal defense against adversarial examples"
    - "scaling provable adversarial defenses"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "bidirectional learning for robust neural networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-2 (induced by source paper "with friends like these who needs adversaries"):
  with members:
    - "classification regions of deep neural networks"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "deepcloak masking deep neural network models for robustness against adversarial samples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "mixup beyond empirical risk minimization"
    - "analysis of universal adversarial perturbations"
    - "opening the black box of deep neural networks via information"
    - "measuring the tendency of cnns to learn surface statistical regularities"

Connected through hyperedge HE-3 (induced by source paper "a new angle on l2 regularization"):
  with members:
    - "robust physical world attacks on deep learning models"
    - "on calibration of modern neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "enhancing robustness of machine learning systems via data transformations"
    - "synthesizing robust adversarial examples"
    - "on the statistical detection of adversarial examples"
    - "practical black box attacks against machine learning"
    - "towards robust deep neural networks with bang"

Connected through hyperedge HE-4 (induced by source paper "blocking transferability of adversarial examples in black box learning systems"):
  with members:
    - "sok security and privacy in machine learning"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "assessing threat of adversarial examples on deep neural networks"
    - "practical black box attacks against machine learning"
    - "towards robust deep neural networks with bang"
    - "adversary resistant deep neural networks with an application to malware detection"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-5 (induced by source paper "robustness of classifiers from adversarial to random noise"):
  with members:
    - "foveation based mechanisms alleviate adversarial examples"
    - "learning with a strong adversary"
    - "adversarial manipulation of deep representations"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "exploring the space of adversarial images"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

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)