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: "high dimensional spaces deep learning and adversarial examples"
  Abstract: In this paper, we analyze deep learning from a mathematical point of view and derive several novel results. The results are based on intriguing mathematical properties of high dimensional spaces. We first look at perturbation based adversarial examples and show how they can be understood using topological arguments in high dimensions. We point out fallacy in an argument presented in a published paper in 2015 by Goodfellow et al., see reference, and present a more rigorous, general and correct mathematical result to explain adversarial examples in terms of image manifolds. Second, we look at optimization landscapes of deep neural networks and examine the number of saddle points relative to that of local minima. Third, we show how multi-resolution nature of images explains perturbation based adversarial examples in form of a stronger result. Our results state that $L_2$-norm of adversarial perturbations shrinks to 0 as image resolution becomes arbitrarily large. Finally, by incorporating the parts-whole manifold learning hypothesis for natural images, we investigate the working of deep neural networks and causes of adversarial examples and discuss how future improvements can be made and how adversarial examples can be eliminated.

Connected through hyperedge HE-1 (induced by source paper "characterizing the decision boundary of deep neural networks"):
  with members:
    - "deep adversarial social recommendation"
    - "learning hierarchical discourse level structure for fake news detection"
    - "deep adversarial network alignment"
    - "on the decision boundaries of deep neural networks a tropical geometry perspective"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "investigating decision boundaries of trained neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "adversarial attacks on deep learning models in natural language processing a survey"

Connected through hyperedge HE-2 (induced by source paper "explaining explanations in ai"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "interpretable explorable approximations of black box models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "challenges for transparency"

Connected through hyperedge HE-3 (induced by source paper "adversarial training versus weight decay"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "adversarial logit pairing"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "predicting adversarial examples with high confidence"
    - "genattack practical black box attacks with gradient free optimization"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-4 (induced by source paper "predicting adversarial examples with high confidence"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "on calibration of modern neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-5 (induced by source paper "adversarial feature genome a data driven adversarial examples recognition method"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial perturbations against real time video classification systems"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-6 (induced by source paper "a mathematical theory of imperfect communication energy efficiency considerations in multi level coding"):
  with members:
    - "a general framework for adversarial examples with objectives"
    - "adversarial spheres"
    - "opening the black box of deep neural networks via information"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "intriguing properties of neural 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.CV(Computer Vision and Pattern Recognition)