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: "robust pre processing a robust defense method against adversary attack"
  Abstract: Deep learning algorithms and networks are vulnerable to perturbed inputs which are known as the adversarial attack. Many defense methodologies have been investigated to defend such adversarial attack. In this work, we propose a novel methodology to defend the existing powerful attack model. Such attack models have achieved record success against MNIST dataset to force it to miss-classify all of its inputs. Whereas Our proposed defense method robust pre-processing achieves the best accuracy among the current state of the art defenses. It consists of Tanh (hyperbolic tangent) function, smoothing and batch normalization to process the input data which will make it more robust over the adversarial attack. robust pre-processing improves the white box attack accuracy of MNIST from 94.3% to 98.7%. Even with increasing defense when others defenses completely fail, robust pre-processing remains one of the strongest ever reported. Another strength of our defense is that it eliminates the need for adversarial training as it can significantly increase the MNIST accuracy without adversarial training as well. This makes it a more generalized defense method with almost half training overhead and much-improved accuracy. robust pre-processing can also increase the inference accuracy in the face of the powerful attack on CIFAR-10 and SVHN data set as well without much sacrificing clean data accuracy.

Connected through hyperedge HE-1 (induced by source paper "adversarial examples in deep learning characterization and divergence"):
  with members:
    - "retrieval augmented convolutional neural networks for improved robustness against adversarial examples"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "blind pre processing a robust defense method against adversarial examples"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "sok security and privacy in machine learning"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-2 (induced by source paper "blind pre processing a robust defense method against adversarial examples"):
  with members:
    - "sok security and privacy in machine learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "enhancing robustness of machine learning systems via data transformations"
    - "globally normalized transition based neural networks"
    - "adversarial examples in the physical world"
    - "defensive distillation is not robust to adversarial examples"
    - "achieving human parity in conversational speech recognition"

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)