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: "cardea context aware visual privacy protection from pervasive cameras"
  Abstract: The growing popularity of mobile and wearable devices with built-in cameras, the bright prospect of camera related applications such as augmented reality and life-logging system, the increased ease of taking and sharing photos, and advances in computer vision techniques have greatly facilitated people's lives in many aspects, but have also inevitably raised people's concerns about visual privacy at the same time. Motivated by recent user studies that people's privacy concerns are dependent on the context, in this paper, we propose Cardea, a context-aware and interactive visual privacy protection framework that enforces privacy protection according to people's privacy preferences. The framework provides people with fine-grained visual privacy protection using: i) personal privacy profiles, with which people can define their context-dependent privacy preferences; and ii) visual indicators: face features, for devices to automatically locate individuals who request privacy protection; and iii) hand gestures, for people to flexibly interact with cameras to temporarily change their privacy preferences. We design and implement the framework consisting of the client app on Android devices and the cloud server. Our evaluation results confirm this framework is practical and effective with 86% overall accuracy, showing promising future for context-aware visual privacy protection from pervasive cameras.

Connected through hyperedge HE-1 (induced by source paper "security and privacy approaches in mixed reality a literature survey"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "magnet a two pronged defense against adversarial examples"
    - "efficient defenses against adversarial attacks"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "cryptoimg privacy preserving processing over encrypted images"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "risk perceptions for wearable devices"
    - "rappor randomized aggregatable privacy preserving ordinal response"

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.CR(Cryptography and Security)