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 "generating optimal privacy protection mechanisms via machine learning"):
  Abstract: We consider the problem of obfuscating sensitive information while preserving utility. Given that an analytical solution is often not feasible because of un-scalability and because the background knowledge may be too complicated to determine, we propose an approach based on machine learning, inspired by the GANs (Generative Adversarial Networks) paradigm. The idea is to set up two nets: the generator, that tries to produce an optimal obfuscation mechanism to protect the data, and the classifier, that tries to de-obfuscate the data. By letting the two nets compete against each other, the mechanism improves its degree of protection, until an equilibrium is reached. We apply our method to the case of location privacy, and we perform experiments on synthetic data and on real data from the Gowalla dataset. We evaluate the privacy of the mechanism not only by its capacity to defeat the classificator, but also in terms of the Bayes error, which represents the strongest possible adversary. We compare the privacy-utility tradeoff of our method with that of the planar Laplace mechanism used in geo-indistinguishability, showing favorable results.

Includes members:
  Member "privacy preserving adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning generative adversarial representations gap under fairness and censoring constraints")
  Member "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memguard defending against black box membership inference attacks via adversarial examples")
  Member "differential privacy as a mutual information constraint":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "on large batch training for deep learning generalization gap and sharp minima":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "privacy games optimal user centric data obfuscation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ensuring privacy in location based services a model based approach")
  Member "optimal geo indistinguishable mechanisms for location privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the long road to computational location privacy a survey")
  Member "geo indistinguishability differential privacy for location based systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "compositional closure for bayes risk in probabilistic noninterference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "abstract hidden markov models a monadic account of quantitative information flow")

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)