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: "defending neural backdoors via generative distribution modeling"
  Abstract: Neural backdoor attack is emerging as a severe security threat to deep learning, while the capability of existing defense methods is limited, especially for complex backdoor triggers. In the work, we explore the space formed by the pixel values of all possible backdoor triggers. An original trigger used by an attacker to build the backdoored model represents only a point in the space. It then will be generalized into a distribution of valid triggers, all of which can influence the backdoored model. Thus, previous methods that model only one point of the trigger distribution is not sufficient. Getting the entire trigger distribution, e.g., via generative modeling, is a key to effective defense. However, existing generative modeling techniques for image generation are not applicable to the backdoor scenario as the trigger distribution is completely unknown. In this work, we propose max-entropy staircase approximator (MESA), an algorithm for high-dimensional sampling-free generative modeling and use it to recover the trigger distribution. We also develop a defense technique to remove the triggers from the backdoored model. Our experiments on Cifar10/100 dataset demonstrate the effectiveness of MESA in modeling the trigger distribution and the robustness of the proposed defense method.

Connected through hyperedge HE-1 (induced by source paper "nnoculation broad spectrum and targeted treatment of backdoored dnns"):
  with members:
    - "are adversarial perturbations a showstopper for ml based cad a case study on cnn based lithographic hotspot detection"
    - "strip a defence against trojan attacks on deep neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "generative adversarial networks an overview"
    - "spectral signatures in backdoor attacks"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "on certifying robustness against backdoor attacks via randomized smoothing"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "strip a defence against trojan attacks on deep neural networks"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-3 (induced by source paper "deep k nn defense against clean label data poisoning attacks"):
  with members:
    - "transfer learning for hate speech detection in social media"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "robustness via curvature regularization and vice versa"
    - "spectral signatures in backdoor attacks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "dual path networks"

Connected through hyperedge HE-4 (induced by source paper "using honeypots to catch adversarial attacks on neural networks"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "are adversarial examples inevitable"
    - "hardware trojan attacks on neural networks"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

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)