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: "active deep learning attacks under strict rate limitations for online api calls"
  Abstract: Machine learning has been applied to a broad range of applications and some of them are available online as application programming interfaces (APIs) with either free (trial) or paid subscriptions. In this paper, we study adversarial machine learning in the form of back-box attacks on online classifier APIs. We start with a deep learning based exploratory (inference) attack, which aims to build a classifier that can provide similar classification results (labels) as the target classifier. To minimize the difference between the labels returned by the inferred classifier and the target classifier, we show that the deep learning based exploratory attack requires a large number of labeled training data samples. These labels can be collected by calling the online API, but usually there is some strict rate limitation on the number of allowed API calls. To mitigate the impact of limited training data, we develop an active learning approach that first builds a classifier based on a small number of API calls and uses this classifier to select samples to further collect their labels. Then, a new classifier is built using more training data samples. This updating process can be repeated multiple times. We show that this active learning approach can build an adversarial classifier with a small statistical difference from the target classifier using only a limited number of training data samples. We further consider evasion and causative (poisoning) attacks based on the inferred classifier that is built by the exploratory attack. Evasion attack determines samples that the target classifier is likely to misclassify, whereas causative attack provides erroneous training data samples to reduce the reliability of the re-trained classifier. The success of these attacks show that adversarial machine learning emerges as a feasible threat in the realistic case with limited training data.

Connected through hyperedge HE-1 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions"):
  with members:
    - "real time and embedded deep learning on fpga for rf signal classification"
    - "adversarial examples in rf deep learning detection of the attack and its physical robustness"
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "adversarial deep learning for over the air spectrum poisoning attacks"
    - "trojan attacks on wireless signal classification with adversarial machine learning"
    - "deepwifi cognitive wifi with deep learning"
    - "when attackers meet ai learning empowered attacks in cooperative spectrum sensing"
    - "generative adversarial networks for black box api attacks with limited training data"

Connected through hyperedge HE-2 (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-3 (induced by source paper "adversarial deep learning for over the air spectrum poisoning attacks"):
  with members:
    - "adversarial examples in rf deep learning detection of the attack and its physical robustness"
    - "spectrum data poisoning with adversarial deep learning"
    - "communication without interception defense against deep learning based modulation detection"
    - "adversarial attacks and defences competition"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-4 (induced by source paper "iot network security from the perspective of adversarial deep learning"):
  with members:
    - "generative adversarial networks for black box api attacks with limited training data"
    - "spectrum data poisoning with adversarial deep learning"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "adversarial attacks on deep learning based radio signal classification"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep sensing cooperative spectrum sensing based on convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "generative adversarial networks for black box api attacks with limited training data"):
  with members:
    - "sok security and privacy in machine learning"
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "stealing machine learning models via prediction apis"
    - "adversarial examples in the physical world"
    - "practical black box attacks against deep learning systems using adversarial examples"
    - "the limitations of deep learning in adversarial settings"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep learning for launching and mitigating wireless jamming attacks"):
  with members:
    - "generative adversarial networks for black box api attacks with limited training data"
    - "spectrum data poisoning with adversarial deep learning"
    - "generative adversarial learning for spectrum sensing"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "deep sensing cooperative spectrum sensing based on convolutional neural networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "learning from simulated and unsupervised images through adversarial training"
    - "convolutional radio modulation recognition 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)