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: "cross entropy loss and low rank features have responsibility for adversarial examples"
  Abstract: State-of-the-art neural networks are vulnerable to adversarial examples; they can easily misclassify inputs that are imperceptibly different than their training and test data. In this work, we establish that the use of cross-entropy loss function and the low-rank features of the training data have responsibility for the existence of these inputs. Based on this observation, we suggest that addressing adversarial examples requires rethinking the use of cross-entropy loss function and looking for an alternative that is more suited for minimization with low-rank features. In this direction, we present a training scheme called differential training, which uses a loss function defined on the differences between the features of points from opposite classes. We show that differential training can ensure a large margin between the decision boundary of the neural network and the points in the training dataset. This larger margin increases the amount of perturbation needed to flip the prediction of the classifier and makes it harder to find an adversarial example with small perturbations. We test differential training on a binary classification task with CIFAR-10 dataset and demonstrate that it radically reduces the ratio of images for which an adversarial example could be found -- not only in the training dataset, but in the test dataset as well.

Connected through hyperedge HE-1 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study"):
  with members:
    - "addressing model vulnerability to distributional shifts over image transformation sets"
    - "testing robustness against unforeseen adversaries"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the attack generator a systematic approach towards constructing adversarial attacks"
    - "deep skinny neural networks are not universal approximators"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-2 (induced by source paper "a restricted black box adversarial framework towards attacking graph embedding models"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial attacks on graph neural networks via meta learning"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "how powerful are graph neural networks"
    - "adversarial attack and defense on graph data a survey"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-3 (induced by source paper "adaptive versus standard descent methods and robustness against adversarial examples"):
  with members:
    - "sgd converges to global minimum in deep learning via star convex path"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training with voronoi constraints"
    - "on the geometry of adversarial examples"
    - "gradient descent finds global minima of deep neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "poincar e glove hyperbolic word embeddings"
    - "are adversarial examples inevitable"

Connected through hyperedge HE-4 (induced by source paper "the general black box attack method for graph neural networks"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "simplifying graph convolutional networks"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "how powerful are graph neural networks"
    - "adversarial attack and defense on graph data a survey"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "motifs in temporal 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)