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: "learning transferable adversarial examples via ghost networks"
  Abstract: The recent development of adversarial attack has proven that ensemble-based methods can perform black-box attack better than the traditional, non-ensemble ones. However, those methods generally suffer from high complexity. They require a family of diverse models, and ensembling them afterward, both of which are computationally expensive. #R##N#In this paper, we propose Ghost Networks to efficiently learn transferable adversarial examples. The key principle of ghost networks is to perturb an existing model, which potentially generates a huge set of diverse models. Those models are subsequently fused by longitudinal ensemble. Both steps almost require no extra time and space consumption. #R##N#Extensive experimental results suggest that the number of networks is essential for improving the transferability of adversarial examples, but it is less necessary to independently train different networks and then ensemble them in an intensive aggregation way. Instead, our work can be a computationally cheap plug-in, which can be easily applied to improve adversarial approaches both in single-model attack and multi-model attack, compatible with both residual and non-residual networks. In particular, by re-producing the NIPS 2017 adversarial competition, our work outperforms the No.1 attack submission by a large margin, which demonstrates its effectiveness and efficiency.

Connected through hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "graph neural networks with generated parameters for relation extraction"
    - "graph few shot learning via knowledge transfer"
    - "graph convolutional networks with eigenpooling"
    - "graph neural networks with high order feature interactions"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"

Connected through hyperedge HE-2 (induced by source paper "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"):
  with members:
    - "adversarial attacks and defences competition"
    - "group normalization"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "universal adversarial training"

Connected through hyperedge HE-3 (induced by source paper "efficient adversarial training with transferable adversarial examples"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "adversarial training for free"
    - "using pre training can improve model robustness and uncertainty"
    - "universal adversarial training"
    - "adversarial robustness toolbox v1 0 0"
    - "certified defenses against adversarial examples"
    - "characterizing deep learning i o workloads in tensorflow"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-4 (induced by source paper "deep neural network ensembles against deception ensemble diversity accuracy and robustness"):
  with members:
    - "denoising and verification cross layer ensemble against black box adversarial attacks"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "adversarial examples in deep learning characterization and divergence"
    - "defense against the dark arts an overview of adversarial example security research and future research directions"
    - "magnet a two pronged defense against adversarial examples"
    - "training ensembles to detect adversarial examples"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-5 (induced by source paper "online bagging for anytime transfer learning"):
  with members:
    - "online bayesian passive aggressive learning"
    - "marginalized denoising autoencoders for domain adaptation"
    - "domain adaptation for statistical classifiers"

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)