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: "training ensembles to detect adversarial examples"
  Abstract: We propose a new ensemble method for detecting and classifying adversarial examples generated by state-of-the-art attacks, including DeepFool and C&W. Our method works by training the members of an ensemble to have low classification error on random benign examples while simultaneously minimizing agreement on examples outside the training distribution. We evaluate on both MNIST and CIFAR-10, against oblivious and both white- and black-box adversaries.

Connected through hyperedge HE-1 (induced by source paper "lower bounds on adversarial robustness from optimal transport"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "theoretically principled trade off between robustness and accuracy"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "analyzing federated learning through an adversarial lens"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-2 (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"
    - "learning transferable adversarial examples via ghost networks"
    - "magnet a two pronged defense against adversarial examples"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-3 (induced by source paper "exploiting the inherent limitation of l0 adversarial examples"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "protecting jpeg images against adversarial attacks"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "adversarial robustness toolbox v0 3 0"
    - "generalizable adversarial examples detection based on bi model decision mismatch"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "generalizable adversarial examples detection based on bi model decision mismatch"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "on the statistical detection of adversarial examples"
    - "delving into transferable adversarial examples and black box attacks"
    - "adversarial examples in the physical world"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "deep residual learning for image recognition"
    - "the limitations of deep learning in adversarial settings"

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)