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: "adversarial image translation unrestricted adversarial examples in face recognition systems"
  Abstract: Thanks to recent advances in Deep Neural Networks (DNNs), face recognition systems have achieved high accuracy in classification of a large number of face images. However, recent works demonstrate that DNNs could be vulnerable to adversarial examples and raise concerns about robustness of face recognition systems. In particular adversarial examples that are not restricted to small perturbations could be more serious risks since conventional certified defenses might be ineffective against them. To shed light on the vulnerability of the face recognition systems to this type of adversarial examples, we propose a flexible and efficient method to generate unrestricted adversarial examples using image translation techniques. Our method enables us to translate a source into any desired facial appearance with large perturbations so that target face recognition systems could be deceived. We demonstrate through our experiments that our method achieves about $90\%$ and $30\%$ attack success rates under a white- and black-box setting, respectively. We also illustrate that our generated images are perceptually realistic and maintain personal identity while the perturbations are large enough to defeat certified defenses.

Connected through hyperedge HE-1 (induced by source paper "detection of face recognition adversarial attacks"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "on the robustness of deep k nearest neighbors"
    - "on adversarial patches real world attack on arcface 100 face recognition system"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "deep face recognition a survey"
    - "attacks on state of the art face recognition using attentional adversarial attack generative network"
    - "picture it in your mind generating high level visual representations from textual descriptions"
    - "adversarial examples are not easily detected bypassing ten detection methods"

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)