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 "reconciling utility and membership privacy via knowledge distillation"):
  Abstract: Large capacity machine learning models are prone to membership inference attacks in which an adversary aims to infer whether a particular data sample is a member of the target model's training dataset. Such membership inferences can lead to serious privacy violations as machine learning models are often trained using privacy-sensitive data such as medical records and controversial user opinions. Recently, defenses against membership inference attacks are developed, in particular, based on differential privacy and adversarial regularization; unfortunately, such defenses highly impact the classification accuracy of the underlying machine learning models. In this work, we present a new defense against membership inference attacks that preserves the utility of the target machine learning models significantly better than prior defenses. Our defense, called distillation for membership privacy (DMP), leverages knowledge distillation to train machine learning models with membership privacy. We analyze the key requirements for membership privacy and provide a novel criterion to select data used for knowledge transfer, in order to improve membership privacy of the final models. DMP works effectively against the attackers with either a whitebox or blackbox access to the target model. We evaluate DMP's performance through extensive experiments on different deep neural networks and using various benchmark datasets. We show that DMP provides a significantly better tradeoff between inference resistance and classification performance than state-of-the-art membership inference defenses. For instance, a DMP-trained DenseNet provides a classification accuracy of 65.3% for a 54.4% blackbox membership inference attack accuracy, while an adversarially regularized DenseNet provides a classification accuracy of only 53.7% for a (much worse) 68.7% blackbox membership inference attack accuracy.

Includes members:
  Member "stolen memories leveraging model memorization for calibrated white box membership inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data and model dependencies of membership inference attack")
  Member "disparate vulnerability on the unfairness of privacy attacks against machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data and model dependencies of membership inference attack")
  Member "evaluating differentially private machine learning in practice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "zero shot knowledge distillation in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative low bitwidth data free quantization")
  Member "exploiting unintended feature leakage in collaborative learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "private model compression via knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bidet an efficient binarized object detector")
  Member "the secret sharer measuring unintended neural network memorization extracting secrets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "membership inference attacks on sequence to sequence models is my data in your machine translation system")
  Member "i know what you see power side channel attack on convolutional neural network accelerators":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints")

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)