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: "on the vulnerability of capsule networks to adversarial attacks"
  Abstract: This paper extensively evaluates the vulnerability of capsule networks to different adversarial attacks. Recent work suggests that these architectures are more robust towards adversarial attacks than other neural networks. However, our experiments show that capsule networks can be fooled as easily as convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "detecting and diagnosing adversarial images with class conditional capsule reconstructions"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "deepcaps going deeper with capsule networks"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "semantic image synthesis with spatially adaptive normalization"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "adversarial attacks and defences competition"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial spheres"

Connected through hyperedge HE-2 (induced by source paper "increasing the adversarial robustness and explainability of capsule networks with gamma capsules"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
    - "3d point capsule networks"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "dynamic routing between capsules"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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)