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: "machine vs machine minimax optimal defense against adversarial examples"
  Abstract: Recently, researchers have discovered that the state-of-the-art object classifiers can be fooled easily by small perturbations in the input unnoticeable to human eyes. It is known that an attacker can generate strong adversarial examples if she knows the classifier parameters. Conversely, a defender can robustify the classifier by retraining if she has the adversarial examples. The cat-and-mouse game nature of attacks and defenses raises the question of the presence of equilibria in the dynamics. In this paper, we present a neural-network based attack class to approximate a larger but intractable class of attacks, and formulate the attacker-defender interaction as a zero-sum leader-follower game. We present sensitivity-penalized optimization algorithms to find minimax solutions, which are the best worst-case defenses against whitebox attacks. Advantages of the learning-based attacks and defenses compared to gradient-based attacks and defenses are demonstrated with MNIST and CIFAR-10.

Connected through hyperedge HE-1 (induced by source paper "motivating the rules of the game for adversarial example research"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "scaling provable adversarial defenses"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "bidirectional learning for robust neural networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-2 (induced by source paper "efficient two step adversarial defense for deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial samples for deep neural networks through mutation testing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "sok security and privacy in machine learning"
    - "magnet a two pronged defense against adversarial examples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "analysis of universal adversarial perturbations"
    - "towards evaluating the robustness 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.LG(Machine Learning)