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: "unifying adversarial training algorithms with flexible deep data gradient regularization"
  Abstract: Many previous proposals for adversarial training of deep neural nets have included di- rectly modifying the gradient, training on a mix of original and adversarial examples, using contractive penalties, and approximately optimizing constrained adversarial ob- jective functions. In this paper, we show these proposals are actually all instances of optimizing a general, regularized objective we call DataGrad. Our proposed DataGrad framework, which can be viewed as a deep extension of the layerwise contractive au- toencoder penalty, cleanly simplifies prior work and easily allows extensions such as adversarial training with multi-task cues. In our experiments, we find that the deep gra- dient regularization of DataGrad (which also has L1 and L2 flavors of regularization) outperforms alternative forms of regularization, including classical L1, L2, and multi- task, both on the original dataset as well as on adversarial sets. Furthermore, we find that combining multi-task optimization with DataGrad adversarial training results in the most robust performance.

Connected through hyperedge HE-1 (induced by source paper "sok security and privacy in machine learning"):
  with members:
    - "poisoning behavioral malware clustering"
    - "is feature selection secure against training data poisoning"
    - "adversarial attacks on neural network policies"
    - "certified defenses for data poisoning attacks"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "algorithmic decision making and the cost of fairness"
    - "adversarial perturbations against deep neural networks for malware classification"

Connected through hyperedge HE-2 (induced by source paper "3 d deep learning approach for remote sensing image classification"):
  with members:
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "densely connected convolutional networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "wide residual networks"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (induced by source paper "interpreting adversarial robustness a view from decision surface in input space"):
  with members:
    - "adversarial logit pairing"
    - "visualizing the loss landscape of neural nets"
    - "mixup beyond empirical risk minimization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "an empirical analysis of the optimization of deep network loss surfaces"
    - "qualitatively characterizing neural network optimization problems"

Connected through hyperedge HE-4 (induced by source paper "learning adversary resistant deep neural networks"):
  with members:
    - "adversarial perturbations against deep neural networks for malware classification"
    - "delving into transferable adversarial examples and black box attacks"
    - "measuring neural net robustness with constraints"
    - "on the effectiveness of defensive distillation"
    - "towards evaluating the robustness of neural networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "adversarial manipulation of deep representations"
    - "the limitations of deep learning in adversarial settings"

Connected through hyperedge HE-5 (induced by source paper "adversary resistant deep neural networks with an application to malware detection"):
  with members:
    - "adversarial perturbations against deep neural networks for malware classification"
    - "defensive distillation is not robust to adversarial examples"
    - "deepdga adversarially tuned domain generation and detection"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "malicious behavior detection using windows audit logs"
    - "towards deep neural network architectures robust to adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "using non invertible data transformations to build adversarial robust neural networks"):
  with members:
    - "defensive distillation is not robust to adversarial examples"
    - "learning with a strong adversary"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "the limitations of deep learning in adversarial settings"
    - "towards deep neural network architectures robust to adversarial examples"
    - "visualizing and understanding convolutional networks"
    - "intriguing properties of neural networks"
    - "scene parsing with multiscale feature learning purity trees and optimal covers"

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)