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 "context aware generative adversarial privacy"):
  Abstract: Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals’ private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP’s performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model; and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.

Includes members:
  Member "estimation efficiency under privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "privacy loss in apple s implementation of differential privacy on macos 10 12":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "information theoretic privacy with general distortion constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matching anonymized and obfuscated time series to users profiles")
  Member "ensemble estimation of mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mint deep network compression via mutual information based neuron trimming")
  Member "deeprotect enabling inference based access control on mobile sensing applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning generative adversarial representations gap under fairness and censoring constraints")
  Member "information potential auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "eigen artificial neural networks")
  Member "privacy aware mmse estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "minimax filter learning to preserve privacy from inference attacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "addressing target shift in zero shot learning using grouped adversarial learning")

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)