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: "explainable learning implicit generative modelling during training for adversarial robustness"
  Abstract: We introduce Explainable Learning ,ExL, an approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative modelling of random noise, during posterior maximization, improves a model's understanding of the data manifold furthering adversarial robustness. We prove our approach's efficacy and provide a simplistic visualization tool for understanding adversarial data, using Principal Component Analysis. Our analysis reveals that adversarial robustness, in general, manifests in models with higher variance along the high-ranked principal components. We show that models learnt with ExL perform remarkably well against a wide-range of black-box attacks.

Connected through hyperedge HE-1 (induced by source paper "discretization based solutions for secure machine learning against adversarial attacks"):
  with members:
    - "implicit generative modeling of random noise during training for adversarial robustness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "attacking binarized neural networks"
    - "adversarial examples in the physical world"
    - "deep residual learning for image recognition"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-2 (induced by source paper "a low effort approach to structured cnn design using pca"):
  with members:
    - "importance estimation for neural network pruning"
    - "discrimination aware channel pruning for deep neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "structured binary neural networks for accurate image classification and semantic segmentation"
    - "nisp pruning networks using neuron importance score propagation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "compression aware training of deep networks"

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)