human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a framework for the extraction of deep neural networks by leveraging public data"):
  Abstract: Machine learning models trained on confidential datasets are increasingly being deployed for profit. Machine Learning as a Service (MLaaS) has made such models easily accessible to end-users. Prior work has developed model extraction attacks, in which an adversary extracts an approximation of MLaaS models by making black-box queries to it. However, none of these works is able to satisfy all the three essential criteria for practical model extraction: (1) the ability to work on deep learning models, (2) the non-requirement of domain knowledge and (3) the ability to work with a limited query budget. We design a model extraction framework that makes use of active learning and large public datasets to satisfy them. We demonstrate that it is possible to use this framework to steal deep classifiers trained on a variety of datasets from image and text domains. By querying a model via black-box access for its top prediction, our framework improves performance on an average over a uniform noise baseline by 4.70x for image tasks and 2.11x for text tasks respectively, while using only 30% (30,000 samples) of the public dataset at its disposal.

Includes members:
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "neural network model extraction attacks in edge devices by hearing architectural hints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary")
  Member "generative adversarial networks for black box api attacks with limited training data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "adversarial active learning for deep networks a margin based approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information condensing active learning")
  Member "cache telepathy leveraging shared resource attacks to learn dnn architectures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints")
  Member "copycat cnn stealing knowledge by persuading confession with random non labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "exploring connections between active learning and model extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cryptanalytic extraction of neural network models")
  Member "mlcapsule guarded offline deployment of machine learning as a service":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "survey of attacks and defenses on edge deployed neural networks")

Question: Please tell me which class the source paper 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)