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: "improving network robustness against adversarial attacks with compact convolution"
  Abstract: Though Convolutional Neural Networks (CNNs) have surpassed human-level performance on tasks such as object classification and face verification, they can easily be fooled by adversarial attacks. These attacks add a small perturbation to the input image that causes the network to mis-classify the sample. In this paper, we focus on neutralizing adversarial attacks by exploring the effect of different loss functions such as CenterLoss and L2-Softmax Loss for enhanced robustness to adversarial perturbations. Additionally, we propose power convolution, a novel method of convolution that when incorporated in conventional CNNs improve their robustness. Power convolution ensures that features at every layer are bounded and close to each other. Extensive experiments show that Power Convolutional Networks (PCNs) neutralize multiple types of attacks, and perform better than existing methods for defending adversarial attacks.

Connected through hyperedge HE-1 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"

Connected through hyperedge HE-2 (induced by source paper "defending against whitebox adversarial attacks via randomized discretization"):
  with members:
    - "adversarial attacks and defences competition"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial logit pairing"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "generating 3d adversarial point clouds"):
  with members:
    - "spatially transformed adversarial examples"
    - "a deeper look at 3d shape classifiers"
    - "adagio interactive experimentation with adversarial attack and defense for audio"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "dynamic graph cnn for learning on point clouds"
    - "generating adversarial examples with adversarial networks"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "shield fast practical defense and vaccination for deep learning using jpeg compression"):
  with members:
    - "provable limitations of deep learning"
    - "dense associative memory is robust to adversarial inputs"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "robust physical world attacks on deep learning models"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "enhancing robustness of machine learning systems via data transformations"

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)