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: "fraternal twins unifying attacks on machine learning and digital watermarking"
  Abstract: Machine learning is increasingly used in security-critical applications, such as autonomous driving, face recognition and malware detection. Most learning methods, however, have not been designed with security in mind and thus are vulnerable to different types of attacks. This problem has motivated the research field of adversarial machine learning that is concerned with attacking and defending learning methods. Concurrently, a different line of research has tackled a very similar problem: In digital watermarking information are embedded in a signal in the presence of an adversary. As a consequence, this research field has also extensively studied techniques for attacking and defending watermarking methods. #R##N#The two research communities have worked in parallel so far, unnoticeably developing similar attack and defense strategies. This paper is a first effort to bring these communities together. To this end, we present a unified notation of black-box attacks against machine learning and watermarking that reveals the similarity of both settings. To demonstrate the efficacy of this unified view, we apply concepts from watermarking to machine learning and vice versa. We show that countermeasures from watermarking can mitigate recent model-extraction attacks and, similarly, that techniques for hardening machine learning can fend off oracle attacks against watermarks. Our work provides a conceptual link between two research fields and thereby opens novel directions for improving the security of both, machine learning and digital watermarking.

Connected through hyperedge HE-1 (induced by source paper "a framework for the extraction of deep neural networks by leveraging public data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "generative adversarial networks for black box api attacks with limited training data"
    - "adversarial active learning for deep networks a margin based approach"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "exploring connections between active learning and model extraction"
    - "mlcapsule guarded offline deployment of machine learning as a service"

Connected through hyperedge HE-2 (induced by source paper "effectiveness of distillation attack and countermeasure on neural network watermarking"):
  with members:
    - "deep learning is robust to massive label noise"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "are you tampering with my data"
    - "embedding watermarks into deep neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adversarial frontier stitching for remote neural network watermarking"

Connected through hyperedge HE-3 (induced by source paper "handling adversarial concept drift in streaming data"):
  with members:
    - "sok security and privacy in machine learning"
    - "security theater on the vulnerability of classifiers to exploratory attacks"
    - "security evaluation of pattern classifiers under attack"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stealing machine learning models via prediction apis"
    - "evasion attacks against machine learning at test time"
    - "scikit learn machine learning in python"
    - "learning under concept drift an overview"

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)